User Authentication Based on Haptic: A Systematic Review

User Authentication Based on Haptic: A Systematic Review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review User Authentication Based on Haptic: A Systematic Review Dr. Ritu Agrawal, Ekagra Agrawal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6543696/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract User authentication is a critical component in securing digital environments, driven by the rapid advancements in the technological revolution. Haptic authentication has emerged as a promising paradigm in the field of user authentication, using tactile feedback and touch-based biometrics for secure and user-friendly access control. This paper presents a systematic literature review of recently published research papers on haptic authentication. This systematic review synthesizes recent advancements in haptic authentication, focusing on methodologies such as touch dynamics, force feedback, and vibrotactile systems. This paper explore their applications, and the challenges associated with their implementation. Challenges such as hardware standardization and data privacy are identified, alongside future opportunities in hybrid models and AI-driven personalization. Through an analysis of peer-reviewed articles, conference proceedings, and patent literature, this review provides a comprehensive overview of haptic authentication's potential to enhance security and discuss future research directions. Haptic Authentication User Authentication Biometrics Tactile Feedback Touch Dynamics Figures Figure 1 Figure 2 Figure 3 1. Introduction Effective user authentication is crucial in today’s digital world, where cyber threats are constantly evolving. User authentication forms the first line of defense in securing the system and maintaining the confidentiality, integrity and availability (CIA) triad of data. Traditional authentication methods, such as passwords and two-factor authentication, have become vulnerable to security breaches, leading to the development of biometric systems. Biometric systems, particularly those based on haptic feedback and touch dynamics, offer a novel solution by utilizing unique physiological and behavioral traits. Haptic is derived from the greek word ‘Haphe’, which means pertaining to the sense of touch. Haptic authentication can be categorized into physiological and behavioral biometrics. Physiological Biometric When haptic systems measure physical characteristics of the body, such as the pressure applied by fingers, finger size, or the unique way a person's skin interacts with a surface, it falls under physiological biometrics. These traits are inherent and relatively unchanging. Behavioral Biometric When haptics analyzes how a person interacts with a device, such as swipe patterns, rhythm, force application, or reaction to vibration cues, it is categorized as behavioral biometrics. These traits are learned and influenced by habits and behaviors. By combining these traits, haptic systems can achieve robust and personalized authentication mechanisms. Haptic technology refers to the use of tactile feedback to simulate the sense of touch in digital environments. It involves applying forces, vibrations, or motions to the user, allowing them to experience and interact with virtual objects through the sensation of touch. This technology is achieved through devices called haptic interfaces, which convert digital signals into physical feedback that users can feel (Salisbury& Srinivasan, 1997 ). This technology has bridged the gap between the virtual and physical worlds. It has a wide range of applications across various fields, enhancing user experiences and improving interaction with digital systems. Its applications are broad, that includes gaming and entertainment, telemedicine and remote surgery, education and training, telerobotics and many more. Many modern devices, such as smartphones and smartwatches, incorporate haptic feedback to provide tactile responses for touch commands (e.g., vibration when typing, receiving notifications, or performing gestures). Devices with haptic-enabled touchscreens or keyboards simulate the feel of pressing a physical button, improving user interaction and reducing errors in typing or navigation. Unlike previous studies that focus on individual authentication techniques, this systematic review consolidates research across multiple haptic modalities, comparing methodologies and success rates while highlighting integration with AI-driven authentication. By analyzing peer-reviewed articles, conference proceedings, and patent literature, this review bridges existing gaps and provides holistic insights into the security implications and real-world applications of haptic authentication. The rest of the paper is closely-knit as follows: Section 2 covers the detailed overview of haptic interface based authentication, including its advantages and challenges, Section 3 reviews prior research in the field. Section 4 outlines the methodology, and Section 5 presents the conclusion. 2. Haptic Interface in User Authentication Touch involves a bidirectional exchange of energy, meaning that when a user applies force to an object, they receive feedback in return—whether that’s pressure, vibration, or resistance. This interaction, sets haptics apart from other modalities because it provides tangible feedback, creating a more immersive and intuitive experience for the user (Srinivasan & LaMotte, 1995 ). As haptic authentication can identify individuals by analyzing unique touch patterns and physiological responses during interaction with a device., PHANToM (Personal Haptic Interface Mechanism) device was developed by SensAble Technologies which is a well-known haptic interface that allows users to interact with virtual objects in a 3D space by providing force feedback. This device is frequently used in fields like virtual reality, medical simulations, and remote robotic control, where users need to feel the texture, shape, or resistance of objects that don't physically exist. The PHANToM uses a stylus or thimble connected to a robotic arm, which tracks the user's movements in 3D space. As the user interacts with virtual objects, the device exerts forces on the stylus, simulating the sensation of touching and manipulating real objects. This creates a more immersive and tactile experience, enhancing realism in virtual environments (Fig. 1 ). 2.1 Haptic Authentication Technologies Touch Dynamics Touch dynamics capture user-specific patterns such as swipe speed, pressure, and touch duration. Advanced machine learning algorithms analyze these patterns to authenticate users, achieving high accuracy rates. Force Feedback : Devices equipped with force feedback can record how users apply pressure or interact with resistance. This interaction creates a unique profile based on the strength, rhythm, and patterns of applied force.These systems are particularly effective in preventing unauthorized access by mimicking physical interactions. Tactile and Vibrotactile Authentication Both these systems use vibration patterns as authentication factors. Custom vibration patterns can be designed, and users’ responses to these patterns (like reaction time or consistency in perceiving vibrations) can serve as a behavioral biometric unique to each individual. These patterns are both perceivable to users and resistant to observation-based attacks, offering a balance between security and usability. Integration with Machine Learning : By combining the data from haptic interactions, advanced algorithms can learn and recognize individual traits to verify identity or detect anomalies. 2.2 Advantages Enhanced Security through Behavioral Biometrics : Touch Dynamics: Haptic interfaces can analyze a user's touch dynamics, such as how they press, swipe, or tap on a touchscreen. Variables like pressure, speed, and rhythm create a unique signature that can be used for authentication. Since these patterns are personalized and continuously evolving, it becomes harder for unauthorized users to mimic. Force and Pressure Sensitivity : Devices equipped with force-sensitive screens or touchpads can detect the unique way users apply pressure. For instance, Apple's "Force Touch" technology can differentiate between different pressure levels, adding an additional layer of authentication beyond the traditional swipe or tap gestures. Gesture-based Authentication : Haptic interfaces can enable gesture recognition, where users perform a specific gesture (such as drawing a pattern or executing a particular swipe sequence) with feedback from the device. These gestures, combined with tactile sensations, are unique to each user, providing an extra layer. Continuous Authentication : Haptic interfaces support continuous authentication, which can monitor user interactions throughout a session rather than only at login. This is crucial for maintaining security, especially in high-risk environments such as online banking or corporate systems. By continuously monitoring a user's touch dynamics or device interactions in real-time, haptic technology can detect anomalies, such as changes in the pressure or way a user swipes, helping to identify unauthorized access attempts. For example, if a different person begins using a device mid-session, subtle differences in touch patterns, grip, or swipe force would raise a security flag, ensuring ongoing protection. In virtual reality (VR) or augmented reality (AR) environments, haptics could track how users interact with virtual objects over time to confirm ongoing authentication. This approach reduces the risk of session hijacking by ensuring that unauthorized individuals cannot take control of a device even after it has been unlocked. Improved Usability and User Experience : Haptic interfaces not only enhance security but also improve usability by offering more intuitive and personalized interactions. The tactile feedback provides users with confirmation when they perform authentication tasks, creating a sense of certainty that their actions have been recognized. For example, when entering a password or drawing a pattern, a user may feel subtle vibrations that confirm each input, reducing errors and increasing confidence. In secure environments, such as online banking or sensitive work systems, haptic feedback can be used to guide users through multi-step authentication processes, ensuring a smoother experience. The personalization potential of haptics also plays a key role. As haptic devices learn how each user interacts with them over time, they can adapt to offer more tailored feedback. This makes authentication systems more user-friendly while maintaining high security. Integration with Emerging Technologies : The growing fields of artificial intelligence (AI) and machine learning (ML) are expected to further enhance the role of haptics in authentication. AI algorithms can learn and refine user interaction patterns, improving the accuracy and adaptability of haptic-based authentication. These systems could detect subtle deviations from normal behavior, flagging potential security threats in real-time. As haptic-based systems track individual behaviors over time, they can learn and adapt to a user’s evolving interaction style. Machine learning algorithms can be employed to refine the authentication model, offering personalized authentication experiences that improve over time. This adaptability increases the system’s accuracy and resilience as it learns user-specific traits such as how their touch or gestures change based on context (e.g., when tired or stressed). Haptic interfaces in user authentication offer a range of advantages and as the technology continues to evolve, its role in securing digital interactions is likely to expand, offering more reliable and intuitive solutions for users and organizations alike. 2.3 Challenges: Variability in User Behavior : One of the main challenges of haptic-based authentication is the inconsistency in how users interact with devices over time. Factors such as mood, stress, fatigue, and the physical environment can all affect how users touch, press, or swipe on a device. Environmental and Device Sensitivity : Haptic-based systems are sensitive to environmental factors such as humidity, temperature, or device cleanliness, which can alter the accuracy of the authentication process. For example, Sweaty or dirty hands may affect the interaction with touchscreens or pressure-sensitive devices, leading to inconsistent readings. Cold environments may alter the user’s grip or touch pressure, further impacting authentication accuracy. Additionally, the quality of the hardware (e.g., pressure sensors or touchscreens) across different devices can vary, affecting the consistency of haptic data collection and reducing the overall performance of the authentication system. Higher False Rejection Rates (FRR) : Haptic-based systems may suffer from higher false rejection rates due to the variability in touch dynamics. Even small differences in how a user interacts with the device can lead to false rejections, which can be frustrating for the user and decrease trust in the system. This is especially problematic in high-security environments where repeated rejections can slow down workflows and cause unnecessary delays. Privacy Concerns : Haptic-based authentication collects sensitive behavioral data, such as touch patterns, pressure levels, and gestures, which can raise privacy concerns. If not properly encrypted and secured, this data could be exploited by attackers or misused by companies, creating potential vulnerabilities. Users may be uncomfortable with the idea that their behavior is being tracked and stored for authentication purposes, particularly if they are unaware of how the data is being used or protected. Moreover, in continuous authentication systems, haptic data is collected throughout a session, potentially exposing more detailed insights into the user’s behavior, raising further privacy issues. Latency and Performance Overhead : Haptic-based authentication, especially when combined with machine learning algorithms that analyze touch patterns in real-time, can introduce latency in the authentication process. For instance, analyzing pressure levels, swipe speeds, and gesture accuracy requires processing power and can slow down the interaction, particularly on mobile or low-powered devices. This can lead to a less responsive system, which negatively impacts the user experience, especially in scenarios where quick authentication is required (e.g., in work environments with frequent system access). 3. Literature Review Research into haptic-authentication has gained momentum as an alternative or complement to traditional biometric and password-based systems. Various studies have explored the role of haptics in user verification by focusing on factors such as touch dynamics, gesture recognition, and force feedback. The work of different researchres is organised by technology wise below. 3.1 Touch-Dynamics Based Authentication Frank et al ( 2012 ) have focused on using touch dynamics in user authenticaton. that is, how users interact with touchscreens through patterns of swipes, taps, and pressure—as a behavioral biometric for authentication. Their studies demonstrated that touch dynamics (such as speed, pressure, and swipe direction) are unique to individuals, making them suitable for authentication purposes. They were able to achieve an 85–97% accuracy in distinguishing users based on their swipe patterns. Agrawal & Sharma ( 2022 ) provided a detailed introduction to touch dynamics biometrics, focusing on behavioral traits such as swipe gestures, typing cadence, and pressure sensitivity. It highlights their potential for distinguishing users in authentication scenarios. While challenges such as environmental sensitivity and demographic diversity remain, the methodology and findings contribute significantly to advancing the field. 3.2 Force-Feedback Authentication These accessibility challenges could prevent some users from effectively interacting with haptic interfaces, making them less inclusive compared to other biometric options like facial or voice recognition.. Kuber & Sharma ( 2010 ) proposed a tactile PIN-based authentication system specifically designed for blind users. They introduced a prototype that uses tactile patterns and force-feedback cues to allow secure, non-visual user verification. While the study was limited in scale and duration, their work is significant because it highlights the accessibility potential of haptic authentication, going beyond traditional biometric systems to support diverse user needs. Potocny et al ( 2015 ) provided valuable insights into how tactile feedback can enhance authentication mechanisms. This paper presented the results of an experiment study on the incorporation of four haptic features (stiffness, static friction, magnetic, and pop-through) in a haptic-enabled authentication procedure. It aligns closely with findings from other research reviewed in this paper, further validating the efficacy of tactile feedback mechanisms. The paper also emphasizes the need for interdisciplinary approaches to address hardware standardization and privacy concerns, which are recurring themes in haptic authentication research. Wazid et al ( 2019 ) conducted a comprehensive study on user authentication within tactile internet-enabled remote surgery environments. Their work emphasized the critical need for mutual authentication mechanisms and session key security protocols that operate reliably in real-time, delay-sensitive scenarios. Although the study was conceptual in nature, it proposed robust authentication architectures tailored for haptic feedback systems in medical settings. The authors identified challenges such as encryption overhead, real-time feedback synchronization, and system resilience under high-stakes conditions. This work expands the scope of haptic-based authentication beyond consumer devices, demonstrating its importance in mission-critical applications like telemedicine and remote robotic surgery, and serves as a foundational reference for future research in secure tactile internet integration 3.3. Cybersecurity-driven continuous authentication and behavioral biometrics Studies on AI-driven authentication models increasingly prioritize haptic-based techniques for secure access control. Strachan & Panëels ( 2016 ), introduced a haptic gesture authentication system,ViSecure. This is a wearable authentication system that integrates gesture recognition with haptic feedback to provide a secure and discreet authentication method. Since authentication relies on haptic feedback rather than visible inputs, it is resistant to shoulder surfing. Georgiev et al ( 2022 ) proposed a range of continuous authentication modeling techniques using touch-based biometrics. Their study is centered on behavioral biometrics and emphasized the importance of ongoing verification during user sessions rather than relying on one-time logins. The authors introduced multi-algorithm ensemble classifiers and RNN-based aggregation models that adapt to dynamic touch behavior across sessions and devices. They evaluated the models using three public datasets, showing that ensemble methods consistently outperformed traditional classification models in both accuracy and adaptability. Their work aligns with the cybersecurity objective of maintaining ongoing user verification while minimizing user friction. Gattulli et al. ( 2023 ) proposed a continuous authentication system for smartphones by analyzing touch events (like taps, swipes, and long presses) in combination with human activity patterns (e.g., walking, sitting, standing). Their study focused on how behavioral signatures—derived from daily phone usage—can act as implicit biometric factors for user verification. The research shows that combining gesture-based touch features with contextual activity data significantly improves authentication accuracy and continuity. Their system emphasizes adaptability, using a multi-sensor fusion strategy that makes it robust to environmental or posture-related changes. Grandi et al. ( 2023 ) proposed a novel continuous authentication method for XR (Extended Reality) environments, integrating Time-Based One-Time Passwords (TOTP), haptic feedback, and kinetic activity tracking. Their system adapts to immersive settings where traditional authentication mechanisms may interrupt user experience. By combining biometric movement patterns with scheduled verification tokens and tactile input, the method ensures ongoing identity verification without breaking immersion. Their study emphasized the growing need for seamless, secure, and adaptive authentication in spatial computing environments.The paper highlights the increasing need for context-aware and device-integrated security in real-time virtual environments. Li et al. ( 2024 ) on VibHead authentication demonstrates how vibration-based authentication enhances security by resisting shoulder-surfing attacks, reinforcing the cybersecurity relevance of haptic authentication. 3.4 AI-Driven Haptic Authentication Olabanji et al ( 2024 ) explored AI-enhanced authentication techniques, including biometric recognition and behavioral analytics. It uses AI-Driven Identity & Access Management (IAM) and discusses continuous authentication models for real-time security. Rehman & Ali ( 2024 ) emphasised on dynamic, adaptive authentication mechanisms in cloud environments. They used machine learning-based models (e.g., decision trees, neural networks) for evaluating access requests in real-time and also discussed context-aware authentication, incorporating location, device, and user behavior. They clearly suggested detailed expansion of AI algorithms used in IAM.Their work also highlighted hardware configurations and security concerns affecting system accuracy. 3.5 Multimodal Integration Liu & Szirányi ( 2021 ) demonstrated combining gesture dynamics with haptic cues using UAVs, employing deep learning techniques such as YOLOv3-tiny for human detection and OpenPose for pose estimation. The system is designed for rescue operations, enabling UAVs to recognize specific body and hand gestures to facilitate communication with individuals in need. Their study achieved 99.8% accuracy in body gesture recognition and 94.71% in hand gesture recognition on testing datasets. They illustrated the importance of reliable human-machine communication in scenarios where traditional input methods may be impractical. Kam & Chin ( 2022 ) conducted a comprehensive review in their paper and explored the integration of haptic and audio feedback in authentication systems, providing a unique perspective on multimodal approaches. While the study is largely theoretical, it contributes a valuable overview of emerging techniques in the field, particularly for systems where visual or traditional biometric modalities are less practical. The paper focused on the security aspects of these methods and strengthens security against common threats like spoofing and observation attacks. 4. Methodology This section provides a description of the Systematic Literature Review (SLR) method, including the methodological procedures followed to conduct the review and analyze studies on haptic-based authentication. Following best practices for systematic reviews, including the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, the process ensures comprehensiveness, reproducibility, and transparency. 4.1. Research Scope The scientific field of this research is haptic technologies, a specialized area within computer science and human-computer interaction (HCI). With cyber threats evolving and conventional authentication methods proving vulnerable, haptic authentication emerges as a highly secure alternative. Traditional methods like passwords and fingerprint-based recognition suffer from spoofing and replay attacks. Haptic-based systems offer a dynamic, behavior-driven alternative that supports continuous verification and aligns with zero-trust security models. As discussed above, haptic authentication encompasses systems that uses tactile feedback, force dynamics, and touch biometrics to enable secure and user-friendly authentication mechanisms. Haptic interactions involve both physiological and behavioral biometrics, allowing the system to recognize users based on their unique touch patterns, pressure levels, and response to vibrotactile feedback. This review focuses on empirical research and technological applications where haptic mechanisms are central to the authentication process. This excludes theoretical frameworks, conceptual proposals, or broader HCI studies not directly tied to authentication. The scope includes authentication systems that utilize haptic feedback across various domains, such as mobile security, virtual and augmented reality (VR/AR), gaming, healthcare authentication, and multimodal biometrics. 4.2. Research Aim and Research Question The research in the field of haptic authentication is to develop and evaluate a haptic authentication system that enhances security and usability in user authentication. Although there has been considerably growing during the last years, yet there is a scarcity of works consolidating the underlined methods. Therefore, the aim of the present analysis is to present a comprehensive and systematic bibliographic review of related work published during the last fifteen years regarding user authentication within the field of haptics, and touch dynamics. The following refined research questions (RQs) were formulated to guide the review process: RQ1. What are the current state-of-the-art haptic-based authentication mechanisms, and how do they compare in terms of security performance (e.g., accuracy, FAR, FRR), usability, and deployment feasibility? RQ2. Which specific haptic modalities (e.g., touch dynamics, force feedback, vibrotactile cues) have been most extensively investigated for user authentication, and in which application domains (e.g., mobile, XR, healthcare) are they predominantly applied? RQ3. What empirical success metrics (e.g., authentication accuracy, equal error rate) and failure factors (e.g., behavioral drift, hardware limitations) are reported across existing haptic authentication schemes? RQ4. What machine learning and deep learning models have been employed in haptic authentication systems, and how do they perform in terms of real-time adaptability, generalization, and resistance to adversarial attacks? RQ5. What are the trade-offs between security robustness, user experience, and computational efficiency in current haptic authentication systems, and how can future designs optimize across these dimensions? 4.3. Systematic Literature Review (SLR) Method The review process followed the PRISMA protocol and included the following stages: Eligibility Criteria : Studies were included if they focused on haptic-based or touch biometrics authentication, provided empirical or theoretical data, and were published between 2010 and 2024. Papers discussing broader human-computer interaction (HCI) concepts without direct links to authentication were omitted. Studies with similar findings from the same research groups were filtered out. Only papers published in English were considered, excluding studies in other languages due to accessibility constraints. Search Strategy : Databases such as IEEE Xplore, PubMed, and Scopus were queried The search string used was as follows: (TITLE-ABS-KEY ((haptic AND authentication) OR (tactile AND feedback) OR (force AND sensing) OR (vibrotactile AND authentication) OR (touch AND biometrics)) AND TITLE(authentication)) 3. Selection Process: Initial results: 587 After duplicate removal: 512 Screened by title/abstract: 401 removed Full-text assessed: 111 Final included studies: 59 empirical studies (Figs. 2 & 3 ) 4. Data Extraction & Synthesis : Extracted fields included authentication modality, methodology, sample size, success metrics, challenges, and ML/DL models used. Results were mapped to the five research questions and tabulated for comparative analysis (see Table 1 in Section 4.4 ). 4.4 Quality Assessment To enhance the rigor and objectivity of the review process, a Quality Assessment Rubric (QAR) was employed to evaluate the methodological soundness and relevance of each selected study. The assessment considered five criteria: publication status, sample size, validation methodology, performance metrics, and relevance to the research questions (RQs). Each study was independently scored, and disagreements were resolved through discussion. Only studies achieving a minimum score of 5 out of 8 were retained for synthesis and comparative analysis. The scoring rules and quality metrics are detailed in Table 1 Table 1 Quality Assessment Rubric Criterion Score Scoring Details Peer Review Status 0–1 1 = Published in peer-reviewed journal/conference; 0 = Otherwise Sample Size 0–2 2 = > 1000 participants; 1 = 100–999; 0 = < 100 or not reported Validation Methodology 0–2 2 = Real-world or cross-validation; 1 = Lab-based or simulated only Performance Metrics Reported 0–1 1 = At least two standard metrics (e.g., Accuracy, EER, FAR, FRR); 0 = Otherwise Relevance to RQs 0–2 2 = Directly addresses RQs; 1 = Partially relevant; 0 = Not aligned Legend PR = Peer Review, SS = Sample Size, VM = Validation Method, PM = Performance Metrics, RQ = Relevance to RQs Table 2 Quality Scores of Included Studies Study PR SS VM PM RQ Total Included Kuber & Sharma ( 2010 ) 1 0 1 1 2 5 Yes Frank et al. ( 2012 ) 1 2 2 1 2 8 Yes Potocny et al. ( 2015 ) 1 1 2 1 2 7 Yes Strachan & Panëels ( 2016 ) 1 1 2 1 2 7 Yes Kaur & Cook ( 2019 ) 1 0 1 1 1 4 No Wazid et al. ( 2019 ) 1 1 2 1 2 7 Yes Yan et al. ( 2020 ) 1 0 1 1 2 5 Yes Liu & Szirányi ( 2021 ) 1 1 2 1 2 7 Yes Agrawal & Sharma ( 2022 ) 1 1 1 1 2 6 Yes Georgiev et al. ( 2022 ) 1 2 2 1 2 8 Yes Kam & Chin ( 2022 ) 1 0 1 1 1 4 No Gattulli et al. ( 2023 ) 1 2 2 1 2 8 Yes Grandi et al. ( 2023 ) 1 1 2 1 2 7 Yes Li et al. ( 2024 ) 1 1 2 1 2 7 Yes Olabanji et al. ( 2024 ) 1 2 2 1 2 8 Yes Rehman & Ali ( 2024 ) 1 2 2 1 2 8 Yes The comparative analysis of research papers included in the systematic review are presented in Table 3 . Table 3 Comparative Analysis of related work Study Focus Area Methodology Dataset Size Success Rate Failure Factors Kuber et al. (2010) Blind User Authentication Tactile PIN-based authentication using a tactile mouse Two-week study with blind users Successful authentication feasibility Interaction time optimization needed Frank et al. (2012) Touch Dynamics Machine Learning (SVM) 3000 samples High (> 90%) Device variability Potocny et al. (2015) Tactile Feedback User Response Analysis 1200 samples High (> 88%) Limited demographic range Strachan & Panëels ( 2016 ) Haptic Gesture Authentication Gesture-based with force feedback Pilot study (N < 50) Feasibility shown Limited scale; sensitivity to posture and lighting Wazid et al. ( 2019 ) Tactile Internet Surgery Secure mutual authentication & session key security TI-enabled surgical setup Improved encryption reliability Latency & real-time feedback challenge Yan et al. ( 2020 ) Haptic Passwords MODWT-based feature extraction & adaptive template scheme 29 subjects High authentication accuracy Forgery-proof performance, but limited sample size Liu & Szirányi ( 2021 ) Gesture Recognition YOLOv3-tiny & OpenPose UAV rescue dataset Body: 99.8%, Hand: 94.71% Environmental limitations, Gesture clarity Agrawal & Sharma ( 2022 ) Touch Dynamics Keystroke and swipe-based behavioral biometrics Moderate sample (N ≈ 100) ~ 89% accuracy Limited to Android; small demographic base Georgiev et al. (2022) Continuous Authentication Multi algorithm ensemble classifier & RNN stacking aggregation Three Public Datasets Outperforms existing touch models Behavioral drift, device heterogeneity Gattulli et al. ( 2023 ) Continuous Authentication Touch Events & Human Activity Recognition H-MOG Dataset 98.9% (1-class SVM) Limited real-world testing Grandi et al. ( 2023 ) XR Authentication Time-Based OTPs, Haptics & Kinetic Activity Experimental XR setup High authentication reliability XR Device Compatibility Li et al. ( 2024 ) VibHead Authentication CNN-based vibration pattern classification through smart headsets Experimental setup with Microsoft HoloLens High authentication accuracy (~ 95%) Device compatibility & vibration consistency Olabanji et al. (2024) AI-Driven IAM AI-enhanced authentication & access control 582 cybersecurity experts surveyed High accuracy in cloud-based IAM Hardware configurations & security concerns Rehman & Ali ( 2024 ) IAM with AI Integration AI-based Behavioral risk models Enterprise datasets (not disclosed) High predictive accuracy Data dependency, regulatory concerns Haptic authentication is transitioning from experimental proofs-of-concept to deployable authentication technologies. With the integration of AI, multimodal input, and continuous authentication, this domain is poised to redefine how users interact securely with devices. Moving forward, the focus must shift toward addressing standardization, cross-device performance, and data privacy to ensure practical, scalable deployment. 5. Conclusion and Future Directions The evolving threat landscape in cybersecurity has exposed vulnerabilities in traditional authentication systems, including passwords, PINs, and even certain biometric techniques like fingerprint recognition. As cyberattacks become more sophisticated, relying solely on static authentication measures is no longer sufficient. Haptic authentication offers a dynamic, behavior-based security solution that enhances protection in several critical ways. This systematic review paper in haptic authentication synthesizes multiple authentication modalities and addresses key gaps in prior studies. This review categorizes methodologies systematically, offering a comparative framework for evaluating effectiveness. It introduces a Multi-Factor Comparative Analysis and a quality-assessed comparison of state-of-the-art systems. This integrates data-driven comparisons of success rates, classification techniques, and authentication precision, ensuring empirical validation. Unlike earlier fragmented discussions, this work provides a structured evaluation of machine learning-driven authentication models. It also highlights challenges beyond technical feasibility: Previous studies focused primarily on algorithm accuracy, but this review expands the discussion to include hardware standardization, privacy implications, and environmental variability. This broader scope ensures practical applicability in real-world deployments. This review suggests next-generation AI-driven adaptive authentication frameworks. Future work should be in development of light-weight, context-aware haptic authentication systems to enable personalized authentication stategies for users. References Agrawal R, Sharma P (2022) A study of touch dynamics biometrics authentication. International Journal of Scientific Research in Engineering and Management (IJSREM) , 6 (7) Frank M, Biedert R, Ma E, Martinovic I, Song D (2012) Touchalytics: On the applicability of touchscreen input as a behavioral biometric for continuous authentication. IEEE Trans Inf Forensics Secur 8(1):136–148 Gattulli V, Impedovo D, Pirlo G, Volpe F (2023) Touch events and human activities for continuous authentication via smartphone. 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IEEE Rehman S, Ali A (2024) AI-Driven Identity and Access Management. Enhancing Authentication and Authorization Security Salisbury JK, Srinivasan MA (1997) Phantom-based haptic interaction with virtual objects. IEEE Comput Graph Appl 17(5):6–10 Srinivasan MA, LaMotte RH (1995) Tactual discrimination of softness. J Neurophysiol 73(1):88–101 Strachan S, Panëels S (2016) ViSecure: A haptic gesture authentication system. In Haptics: Perception, Devices, Control, and Applications: 10th International Conference, EuroHaptics 2016, London, UK, July 4–7, 2016, Proceedings, Part II 10 (pp. 177–186). Springer International Publishing Wazid M, Das AK, Lee JH (2019) User authentication in a tactile internet based remote surgery environment: Security issues, challenges, and future research directions. Pervasive Mob Comput 54:71–85 Yan J, Bonaci T, Chizeck HJ (2020) Your signature is your password: Haptic passwords on mobile devices. arXiv preprint arXiv:2010.14007 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6543696","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":448811214,"identity":"61a35eaa-fb50-4e5d-95e2-50bb99ac0780","order_by":0,"name":"Dr. Ritu Agrawal","email":"","orcid":"https://orcid.org/0009-0003-7509-4873","institution":"Institute of Management \u0026 Technology, Faridabad","correspondingAuthor":false,"prefix":"Dr.","firstName":"Ritu","middleName":"","lastName":"Agrawal","suffix":""},{"id":448811215,"identity":"16cb3958-ef8b-42d3-80af-943372c84ee6","order_by":1,"name":"Ekagra Agrawal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYDACHmTOgwogwczcQIKWhDMgLYykaElsA5EEtPDzHH78uqLGzm57+xnDD4nzaqP524FaflRsw6lFsrfNzPLMseTkOWdyjCUStx3PnXGYsYGx58xtnFoMzjOYGTawMSdLMORuAGo5ltsA1MLM2IZbi/159m+GDf/qkyX4327+kTjnWO58QloMeHuMHza2HbaTkMjdJpHYUJO7gZAWiTNnyhgb+44nSEi8/2aRcOxA7kagloP4/MLfk775Y8O3ansJ/rTkGx9q6nLnnT988MGPCtxagIBNAkgkNkA4h8HkAXzqgYD5A5Cwh3LqCCgeBaNgFIyCkQgAlsFfZPw0PLMAAAAASUVORK5CYII=","orcid":"","institution":"Manipal University Jaipur","correspondingAuthor":true,"prefix":"","firstName":"Ekagra","middleName":"","lastName":"Agrawal","suffix":""}],"badges":[],"createdAt":"2025-04-28 04:14:33","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6543696/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6543696/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81627866,"identity":"cc95781a-ae1b-40cc-9b40-395cc55a3987","added_by":"auto","created_at":"2025-04-29 10:46:36","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":14287,"visible":true,"origin":"","legend":"\u003cp\u003ePHANToM Desktop Haptic Device\u003c/p\u003e","description":"","filename":"groupimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6543696/v1/f399cf6586270079b2e6eccd.jpeg"},{"id":81627864,"identity":"4b50f709-1dd2-469e-9acc-e2dfb8a06d05","added_by":"auto","created_at":"2025-04-29 10:46:36","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":37687,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA Flowchart\u003c/p\u003e","description":"","filename":"groupimage22.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6543696/v1/fe60de642c48df2522a36d6c.jpeg"},{"id":81629126,"identity":"139ff2e0-16e1-497f-9dee-9cbe1179f261","added_by":"auto","created_at":"2025-04-29 11:02:36","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":43998,"visible":true,"origin":"","legend":"\u003cp\u003eYearly Distribution of papers included in our work\u003c/p\u003e","description":"","filename":"groupimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6543696/v1/ae68bfad67d0394c5c1f05a4.jpeg"},{"id":81629869,"identity":"23abcb7d-1e87-41d5-bb1c-9cfdbb7cfbdf","added_by":"auto","created_at":"2025-04-29 11:10:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1182425,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6543696/v1/a5b454be-78e0-49a4-b169-c4ac886aaa14.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eUser Authentication Based on Haptic: A Systematic Review\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEffective user authentication is crucial in today’s digital world, where cyber threats are constantly evolving. User authentication forms the first line of defense in securing the system and maintaining the confidentiality, integrity and availability (CIA) triad of data. Traditional authentication methods, such as passwords and two-factor authentication, have become vulnerable to security breaches, leading to the development of biometric systems. Biometric systems, particularly those based on haptic feedback and touch dynamics, offer a novel solution by utilizing unique physiological and behavioral traits.\u003c/p\u003e \u003cp\u003eHaptic is derived from the greek word ‘Haphe’, which means pertaining to the sense of touch. Haptic authentication can be categorized into physiological and behavioral biometrics.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePhysiological Biometric\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eWhen haptic systems measure physical characteristics of the body, such as the pressure applied by fingers, finger size, or the unique way a person's skin interacts with a surface, it falls under physiological biometrics. These traits are inherent and relatively unchanging.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBehavioral Biometric\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eWhen haptics analyzes how a person interacts with a device, such as swipe patterns, rhythm, force application, or reaction to vibration cues, it is categorized as behavioral biometrics. These traits are learned and influenced by habits and behaviors.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eBy combining these traits, haptic systems can achieve robust and personalized authentication mechanisms.\u003c/p\u003e \u003cp\u003eHaptic technology refers to the use of tactile feedback to simulate the sense of touch in digital environments. It involves applying forces, vibrations, or motions to the user, allowing them to experience and interact with virtual objects through the sensation of touch. This technology is achieved through devices called haptic interfaces, which convert digital signals into physical feedback that users can feel (Salisbury\u0026amp; Srinivasan, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis technology has bridged the gap between the virtual and physical worlds. It has a wide range of applications across various fields, enhancing user experiences and improving interaction with digital systems. Its applications are broad, that includes gaming and entertainment, telemedicine and remote surgery, education and training, telerobotics and many more. Many modern devices, such as smartphones and smartwatches, incorporate haptic feedback to provide tactile responses for touch commands (e.g., vibration when typing, receiving notifications, or performing gestures). Devices with haptic-enabled touchscreens or keyboards simulate the feel of pressing a physical button, improving user interaction and reducing errors in typing or navigation.\u003c/p\u003e \u003cp\u003eUnlike previous studies that focus on individual authentication techniques, this systematic review consolidates research across multiple haptic modalities, comparing methodologies and success rates while highlighting integration with AI-driven authentication. By analyzing peer-reviewed articles, conference proceedings, and patent literature, this review bridges existing gaps and provides holistic insights into the security implications and real-world applications of haptic authentication.\u003c/p\u003e \u003cp\u003eThe rest of the paper is closely-knit as follows: Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e covers the detailed overview of haptic interface based authentication, including its advantages and challenges, Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e3\u003c/span\u003e reviews prior research in the field. Section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e4\u003c/span\u003e outlines the methodology, and Section \u003cspan refid=\"Sec18\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the conclusion.\u003c/p\u003e"},{"header":"2. Haptic Interface in User Authentication","content":"\u003cp\u003eTouch involves a bidirectional exchange of energy, meaning that when a user applies force to an object, they receive feedback in return—whether that’s pressure, vibration, or resistance. This interaction, sets haptics apart from other modalities because it provides tangible feedback, creating a more immersive and intuitive experience for the user (Srinivasan \u0026amp; LaMotte, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAs haptic authentication can identify individuals by analyzing unique touch patterns and physiological responses during interaction with a device., PHANToM (Personal Haptic Interface Mechanism) device was developed by SensAble Technologies which is a well-known haptic interface that allows users to interact with virtual objects in a 3D space by providing force feedback. This device is frequently used in fields like virtual reality, medical simulations, and remote robotic control, where users need to feel the texture, shape, or resistance of objects that don't physically exist. The PHANToM uses a stylus or thimble connected to a robotic arm, which tracks the user's movements in 3D space. As the user interacts with virtual objects, the device exerts forces on the stylus, simulating the sensation of touching and manipulating real objects. This creates a more immersive and tactile experience, enhancing realism in virtual environments (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003e2.1 Haptic Authentication Technologies\u003c/h2\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTouch Dynamics\u003c/b\u003e Touch dynamics capture user-specific patterns such as swipe speed, pressure, and touch duration. Advanced machine learning algorithms analyze these patterns to authenticate users, achieving high accuracy rates.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eForce Feedback\u003c/b\u003e: Devices equipped with force feedback can record how users apply pressure or interact with resistance. This interaction creates a unique profile based on the strength, rhythm, and patterns of applied force.These systems are particularly effective in preventing unauthorized access by mimicking physical interactions.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTactile and Vibrotactile Authentication\u003c/b\u003e Both these systems use vibration patterns as authentication factors. Custom vibration patterns can be designed, and users’ responses to these patterns (like reaction time or consistency in perceiving vibrations) can serve as a behavioral biometric unique to each individual. These patterns are both perceivable to users and resistant to observation-based attacks, offering a balance between security and usability.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIntegration with Machine Learning\u003c/b\u003e: By combining the data from haptic interactions, advanced algorithms can learn and recognize individual traits to verify identity or detect anomalies.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003ch3\u003e2.2 Advantages\u003c/h3\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEnhanced Security through Behavioral Biometrics\u003c/b\u003e: Touch Dynamics: Haptic interfaces can analyze a user's touch dynamics, such as how they press, swipe, or tap on a touchscreen. Variables like pressure, speed, and rhythm create a unique signature that can be used for authentication. Since these patterns are personalized and continuously evolving, it becomes harder for unauthorized users to mimic.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eForce and Pressure Sensitivity\u003c/b\u003e: Devices equipped with force-sensitive screens or touchpads can detect the unique way users apply pressure. For instance, Apple's \"Force Touch\" technology can differentiate between different pressure levels, adding an additional layer of authentication beyond the traditional swipe or tap gestures.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eGesture-based Authentication\u003c/b\u003e: Haptic interfaces can enable gesture recognition, where users perform a specific gesture (such as drawing a pattern or executing a particular swipe sequence) with feedback from the device. These gestures, combined with tactile sensations, are unique to each user, providing an extra layer.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eContinuous Authentication\u003c/b\u003e: Haptic interfaces support continuous authentication, which can monitor user interactions throughout a session rather than only at login. This is crucial for maintaining security, especially in high-risk environments such as online banking or corporate systems. By continuously monitoring a user's touch dynamics or device interactions in real-time, haptic technology can detect anomalies, such as changes in the pressure or way a user swipes, helping to identify unauthorized access attempts. For example, if a different person begins using a device mid-session, subtle differences in touch patterns, grip, or swipe force would raise a security flag, ensuring ongoing protection. In virtual reality (VR) or augmented reality (AR) environments, haptics could track how users interact with virtual objects over time to confirm ongoing authentication. This approach reduces the risk of session hijacking by ensuring that unauthorized individuals cannot take control of a device even after it has been unlocked.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eImproved Usability and User Experience\u003c/b\u003e: Haptic interfaces not only enhance security but also improve usability by offering more intuitive and personalized interactions. The tactile feedback provides users with confirmation when they perform authentication tasks, creating a sense of certainty that their actions have been recognized. For example, when entering a password or drawing a pattern, a user may feel subtle vibrations that confirm each input, reducing errors and increasing confidence. In secure environments, such as online banking or sensitive work systems, haptic feedback can be used to guide users through multi-step authentication processes, ensuring a smoother experience. The personalization potential of haptics also plays a key role. As haptic devices learn how each user interacts with them over time, they can adapt to offer more tailored feedback. This makes authentication systems more user-friendly while maintaining high security.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIntegration with Emerging Technologies\u003c/b\u003e: The growing fields of artificial intelligence (AI) and machine learning (ML) are expected to further enhance the role of haptics in authentication. AI algorithms can learn and refine user interaction patterns, improving the accuracy and adaptability of haptic-based authentication. These systems could detect subtle deviations from normal behavior, flagging potential security threats in real-time. As haptic-based systems track individual behaviors over time, they can learn and adapt to a user’s evolving interaction style. Machine learning algorithms can be employed to refine the authentication model, offering personalized authentication experiences that improve over time. This adaptability increases the system’s accuracy and resilience as it learns user-specific traits such as how their touch or gestures change based on context (e.g., when tired or stressed).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003cp\u003eHaptic interfaces in user authentication offer a range of advantages and as the technology continues to evolve, its role in securing digital interactions is likely to expand, offering more reliable and intuitive solutions for users and organizations alike.\u003c/p\u003e\u003ch3\u003e2.3 Challenges:\u003c/h3\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eVariability in User Behavior\u003c/b\u003e: One of the main challenges of haptic-based authentication is the inconsistency in how users interact with devices over time. Factors such as mood, stress, fatigue, and the physical environment can all affect how users touch, press, or swipe on a device.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEnvironmental and Device Sensitivity\u003c/b\u003e: Haptic-based systems are sensitive to environmental factors such as humidity, temperature, or device cleanliness, which can alter the accuracy of the authentication process. For example, Sweaty or dirty hands may affect the interaction with touchscreens or pressure-sensitive devices, leading to inconsistent readings. Cold environments may alter the user’s grip or touch pressure, further impacting authentication accuracy. Additionally, the quality of the hardware (e.g., pressure sensors or touchscreens) across different devices can vary, affecting the consistency of haptic data collection and reducing the overall performance of the authentication system.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eHigher False Rejection Rates (FRR)\u003c/b\u003e: Haptic-based systems may suffer from higher false rejection rates due to the variability in touch dynamics. Even small differences in how a user interacts with the device can lead to false rejections, which can be frustrating for the user and decrease trust in the system. This is especially problematic in high-security environments where repeated rejections can slow down workflows and cause unnecessary delays.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePrivacy Concerns\u003c/b\u003e: Haptic-based authentication collects sensitive behavioral data, such as touch patterns, pressure levels, and gestures, which can raise privacy concerns. If not properly encrypted and secured, this data could be exploited by attackers or misused by companies, creating potential vulnerabilities. Users may be uncomfortable with the idea that their behavior is being tracked and stored for authentication purposes, particularly if they are unaware of how the data is being used or protected. Moreover, in continuous authentication systems, haptic data is collected throughout a session, potentially exposing more detailed insights into the user’s behavior, raising further privacy issues.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLatency and Performance Overhead\u003c/b\u003e: Haptic-based authentication, especially when combined with machine learning algorithms that analyze touch patterns in real-time, can introduce latency in the authentication process. For instance, analyzing pressure levels, swipe speeds, and gesture accuracy requires processing power and can slow down the interaction, particularly on mobile or low-powered devices. This can lead to a less responsive system, which negatively impacts the user experience, especially in scenarios where quick authentication is required (e.g., in work environments with frequent system access).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e"},{"header":"3. Literature Review","content":"\u003cp\u003eResearch into haptic-authentication has gained momentum as an alternative or complement to traditional biometric and password-based systems. Various studies have explored the role of haptics in user verification by focusing on factors such as touch dynamics, gesture recognition, and force feedback. The work of different researchres is organised by technology wise below.\u003c/p\u003e\u003ch3\u003e3.1 Touch-Dynamics Based Authentication\u003c/h3\u003e\u003cp\u003eFrank et al (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) have focused on using touch dynamics in user authenticaton. that is, how users interact with touchscreens through patterns of swipes, taps, and pressure—as a behavioral biometric for authentication. Their studies demonstrated that touch dynamics (such as speed, pressure, and swipe direction) are unique to individuals, making them suitable for authentication purposes. They were able to achieve an 85–97% accuracy in distinguishing users based on their swipe patterns.\u003c/p\u003e\u003cp\u003eAgrawal \u0026amp; Sharma (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) provided a detailed introduction to touch dynamics biometrics, focusing on behavioral traits such as swipe gestures, typing cadence, and pressure sensitivity. It highlights their potential for distinguishing users in authentication scenarios. While challenges such as environmental sensitivity and demographic diversity remain, the methodology and findings contribute significantly to advancing the field.\u003c/p\u003e\u003ch2\u003e3.2 Force-Feedback Authentication\u003c/h2\u003e\u003cp\u003eThese accessibility challenges could prevent some users from effectively interacting with haptic interfaces, making them less inclusive compared to other biometric options like facial or voice recognition..\u003c/p\u003e\u003cp\u003eKuber \u0026amp; Sharma (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) proposed a tactile PIN-based authentication system specifically designed for blind users. They introduced a prototype that uses tactile patterns and force-feedback cues to allow secure, non-visual user verification. While the study was limited in scale and duration, their work is significant because it highlights the accessibility potential of haptic authentication, going beyond traditional biometric systems to support diverse user needs.\u003c/p\u003e\u003cp\u003ePotocny et al (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) provided valuable insights into how tactile feedback can enhance authentication mechanisms. This paper presented the results of an experiment study on the incorporation of four haptic features (stiffness, static friction, magnetic, and pop-through) in a haptic-enabled authentication procedure. It aligns closely with findings from other research reviewed in this paper, further validating the efficacy of tactile feedback mechanisms. The paper also emphasizes the need for interdisciplinary approaches to address hardware standardization and privacy concerns, which are recurring themes in haptic authentication research.\u003c/p\u003e\u003cp\u003eWazid et al (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) conducted a comprehensive study on user authentication within tactile internet-enabled remote surgery environments. Their work emphasized the critical need for mutual authentication mechanisms and session key security protocols that operate reliably in real-time, delay-sensitive scenarios. Although the study was conceptual in nature, it proposed robust authentication architectures tailored for haptic feedback systems in medical settings. The authors identified challenges such as encryption overhead, real-time feedback synchronization, and system resilience under high-stakes conditions. This work expands the scope of haptic-based authentication beyond consumer devices, demonstrating its importance in mission-critical applications like telemedicine and remote robotic surgery, and serves as a foundational reference for future research in secure tactile internet integration\u003c/p\u003e\u003ch3\u003e3.3. Cybersecurity-driven continuous authentication and behavioral biometrics\u003c/h3\u003e\u003cp\u003eStudies on AI-driven authentication models increasingly prioritize haptic-based techniques for secure access control.\u003c/p\u003e\u003cp\u003eStrachan \u0026amp; Panëels (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), introduced a haptic gesture authentication system,ViSecure. This is a wearable authentication system that integrates gesture recognition with haptic feedback to provide a secure and discreet authentication method. Since authentication relies on haptic feedback rather than visible inputs, it is resistant to shoulder surfing.\u003c/p\u003e\u003cp\u003eGeorgiev et al (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) proposed a range of continuous authentication modeling techniques using touch-based biometrics. Their study is centered on behavioral biometrics and emphasized the importance of ongoing verification during user sessions rather than relying on one-time logins. The authors introduced multi-algorithm ensemble classifiers and RNN-based aggregation models that adapt to dynamic touch behavior across sessions and devices. They evaluated the models using three public datasets, showing that ensemble methods consistently outperformed traditional classification models in both accuracy and adaptability. Their work aligns with the cybersecurity objective of maintaining ongoing user verification while minimizing user friction.\u003c/p\u003e\u003cp\u003eGattulli et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) proposed a continuous authentication system for smartphones by analyzing touch events (like taps, swipes, and long presses) in combination with human activity patterns (e.g., walking, sitting, standing). Their study focused on how behavioral signatures—derived from daily phone usage—can act as implicit biometric factors for user verification. The research shows that combining gesture-based touch features with contextual activity data significantly improves authentication accuracy and continuity. Their system emphasizes adaptability, using a multi-sensor fusion strategy that makes it robust to environmental or posture-related changes.\u003c/p\u003e\u003cp\u003eGrandi et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) proposed a novel continuous authentication method for XR (Extended Reality) environments, integrating Time-Based One-Time Passwords (TOTP), haptic feedback, and kinetic activity tracking. Their system adapts to immersive settings where traditional authentication mechanisms may interrupt user experience. By combining biometric movement patterns with scheduled verification tokens and tactile input, the method ensures ongoing identity verification without breaking immersion. Their study emphasized the growing need for seamless, secure, and adaptive authentication in spatial computing environments.The paper highlights the increasing need for context-aware and device-integrated security in real-time virtual environments.\u003c/p\u003e\u003cp\u003eLi et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) on VibHead authentication demonstrates how vibration-based authentication enhances security by resisting shoulder-surfing attacks, reinforcing the cybersecurity relevance of haptic authentication.\u003c/p\u003e\u003ch3\u003e3.4 AI-Driven Haptic Authentication\u003c/h3\u003e\u003cp\u003eOlabanji et al (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) explored AI-enhanced authentication techniques, including biometric recognition and behavioral analytics. It uses AI-Driven Identity \u0026amp; Access Management (IAM) and discusses continuous authentication models for real-time security.\u003c/p\u003e\u003cp\u003eRehman \u0026amp; Ali (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) emphasised on dynamic, adaptive authentication mechanisms in cloud environments. They used machine learning-based models (e.g., decision trees, neural networks) for evaluating access requests in real-time and also discussed context-aware authentication, incorporating location, device, and user behavior. They clearly suggested detailed expansion of AI algorithms used in IAM.Their work also highlighted hardware configurations and security concerns affecting system accuracy.\u003c/p\u003e\u003ch2\u003e3.5 Multimodal Integration\u003c/h2\u003e\u003cp\u003eLiu \u0026amp; Szirányi (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) demonstrated combining gesture dynamics with haptic cues using UAVs, employing deep learning techniques such as YOLOv3-tiny for human detection and OpenPose for pose estimation. The system is designed for rescue operations, enabling UAVs to recognize specific body and hand gestures to facilitate communication with individuals in need. Their study achieved 99.8% accuracy in body gesture recognition and 94.71% in hand gesture recognition on testing datasets. They illustrated the importance of reliable human-machine communication in scenarios where traditional input methods may be impractical.\u003c/p\u003e\u003cp\u003eKam \u0026amp; Chin (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) conducted a comprehensive review in their paper and explored the integration of haptic and audio feedback in authentication systems, providing a unique perspective on multimodal approaches. While the study is largely theoretical, it contributes a valuable overview of emerging techniques in the field, particularly for systems where visual or traditional biometric modalities are less practical. The paper focused on the security aspects of these methods and strengthens security against common threats like spoofing and observation attacks.\u003c/p\u003e"},{"header":"4. Methodology","content":"\u003cp\u003eThis section provides a description of the Systematic Literature Review (SLR) method, including the methodological procedures followed to conduct the review and analyze studies on haptic-based authentication. Following best practices for systematic reviews, including the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, the process ensures comprehensiveness, reproducibility, and transparency.\u003c/p\u003e\u003ch2\u003e4.1. Research Scope\u003c/h2\u003e\u003cp\u003eThe scientific field of this research is haptic technologies, a specialized area within computer science and human-computer interaction (HCI). With cyber threats evolving and conventional authentication methods proving vulnerable, haptic authentication emerges as a highly secure alternative. Traditional methods like passwords and fingerprint-based recognition suffer from spoofing and replay attacks. Haptic-based systems offer a dynamic, behavior-driven alternative that supports continuous verification and aligns with zero-trust security models.\u003c/p\u003e\u003cp\u003eAs discussed above, haptic authentication encompasses systems that uses tactile feedback, force dynamics, and touch biometrics to enable secure and user-friendly authentication mechanisms. Haptic interactions involve both physiological and behavioral biometrics, allowing the system to recognize users based on their unique touch patterns, pressure levels, and response to vibrotactile feedback.\u003c/p\u003e\u003cp\u003eThis review focuses on empirical research and technological applications where haptic mechanisms are central to the authentication process. This excludes theoretical frameworks, conceptual proposals, or broader HCI studies not directly tied to authentication. The scope includes authentication systems that utilize haptic feedback across various domains, such as mobile security, virtual and augmented reality (VR/AR), gaming, healthcare authentication, and multimodal biometrics.\u003c/p\u003e\u003ch2\u003e4.2. Research Aim and Research Question\u003c/h2\u003e\u003cp\u003eThe research in the field of haptic authentication is to develop and evaluate a haptic authentication system that enhances security and usability in user authentication. Although there has been considerably growing during the last years, yet there is a scarcity of works consolidating the underlined methods. Therefore, the aim of the present analysis is to present a comprehensive and systematic bibliographic review of related work published during the last fifteen years regarding user authentication within the field of haptics, and touch dynamics.\u003c/p\u003e\u003cp\u003eThe following refined research questions (RQs) were formulated to guide the review process:\u003c/p\u003e\u003cp\u003eRQ1. What are the current state-of-the-art haptic-based authentication mechanisms, and how do they compare in terms of security performance (e.g., accuracy, FAR, FRR), usability, and deployment feasibility?\u003c/p\u003e\u003cp\u003eRQ2. Which specific haptic modalities (e.g., touch dynamics, force feedback, vibrotactile cues) have been most extensively investigated for user authentication, and in which application domains (e.g., mobile, XR, healthcare) are they predominantly applied?\u003c/p\u003e\u003cp\u003eRQ3. What empirical success metrics (e.g., authentication accuracy, equal error rate) and failure factors (e.g., behavioral drift, hardware limitations) are reported across existing haptic authentication schemes?\u003c/p\u003e\u003cp\u003eRQ4. What machine learning and deep learning models have been employed in haptic authentication systems, and how do they perform in terms of real-time adaptability, generalization, and resistance to adversarial attacks?\u003c/p\u003e\u003cp\u003eRQ5. What are the trade-offs between security robustness, user experience, and computational efficiency in current haptic authentication systems, and how can future designs optimize across these dimensions?\u003c/p\u003e\u003ch2\u003e4.3. Systematic Literature Review (SLR) Method\u003c/h2\u003e\u003cp\u003eThe review process followed the PRISMA protocol and included the following stages:\u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEligibility Criteria\u003c/b\u003e: Studies were included if they focused on haptic-based or touch biometrics authentication, provided empirical or theoretical data, and were published between 2010 and 2024. Papers discussing broader human-computer interaction (HCI) concepts without direct links to authentication were omitted. Studies with similar findings from the same research groups were filtered out. Only papers published in English were considered, excluding studies in other languages due to accessibility constraints.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSearch Strategy\u003c/b\u003e: Databases such as IEEE Xplore, PubMed, and Scopus were queried\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e\u003cp\u003eThe search string used was as follows:\u003c/p\u003e\u003cp\u003e(TITLE-ABS-KEY ((haptic AND authentication) OR (tactile AND feedback) OR (force AND sensing) OR (vibrotactile AND authentication) OR (touch AND biometrics)) AND TITLE(authentication))\u003c/p\u003e\u003ch2\u003e3. Selection Process:\u003c/h2\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eInitial results: 587\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAfter duplicate removal: 512\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eScreened by title/abstract: 401 removed\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFull-text assessed: 111\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFinal included studies: 59 empirical studies (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u0026amp; \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cp\u003e \u003cb\u003e4. Data Extraction \u0026amp; Synthesis\u003c/b\u003e: Extracted fields included authentication modality, methodology, sample size, success metrics, challenges, and ML/DL models used. Results were mapped to the five research questions and tabulated for comparative analysis (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e in Section \u003cspan refid=\"Sec17\" class=\"InternalRef\"\u003e4.4\u003c/span\u003e).\u003c/p\u003e \u003ch2\u003e4.4 Quality Assessment\u003c/h2\u003e\u003cp\u003eTo enhance the rigor and objectivity of the review process, a Quality Assessment Rubric (QAR) was employed to evaluate the methodological soundness and relevance of each selected study. The assessment considered five criteria: publication status, sample size, validation methodology, performance metrics, and relevance to the research questions (RQs). Each study was independently scored, and disagreements were resolved through discussion.\u003c/p\u003e\u003cp\u003eOnly studies achieving a minimum score of 5 out of 8 were retained for synthesis and comparative analysis. The scoring rules and quality metrics are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eQuality Assessment Rubric\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriterion\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScoring Details\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePeer Review Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0–1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 = Published in peer-reviewed journal/conference; 0 = Otherwise\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSample Size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0–2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 = \u0026gt; 1000 participants; 1 = 100–999; 0 = \u0026lt; 100 or not reported\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eValidation Methodology\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0–2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 = Real-world or cross-validation; 1 = Lab-based or simulated only\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerformance Metrics Reported\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0–1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 = At least two standard metrics (e.g., Accuracy, EER, FAR, FRR); 0 = Otherwise\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRelevance to RQs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0–2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 = Directly addresses RQs; 1 = Partially relevant; 0 = Not aligned\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e \u003cstrong\u003eLegend\u003c/strong\u003e \u003c/p\u003e\u003cp\u003ePR = Peer Review, SS = Sample Size, VM = Validation Method, PM = Performance Metrics, RQ = Relevance to RQs\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eQuality Scores of Included Studies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVM\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePM\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRQ\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIncluded\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKuber \u0026amp; Sharma (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrank et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotocny et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrachan \u0026amp; Panëels (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKaur \u0026amp; Cook (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWazid et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYan et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiu \u0026amp; Szirányi (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgrawal \u0026amp; Sharma (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeorgiev et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKam \u0026amp; Chin (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGattulli et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrandi et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLi et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOlabanji et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRehman \u0026amp; Ali (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eThe comparative analysis of research papers included in the systematic review are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparative Analysis of related work\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFocus Area\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMethodology\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDataset Size\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSuccess Rate\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFailure Factors\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKuber et al. (2010)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlind User Authentication\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTactile PIN-based authentication using a tactile mouse\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTwo-week study with blind users\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSuccessful authentication feasibility\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInteraction time optimization needed\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrank et al.\u003c/p\u003e \u003cp\u003e(2012)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTouch Dynamics\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMachine Learning (SVM)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3000 samples\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh (\u0026gt; 90%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDevice variability\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotocny et al.\u003c/p\u003e \u003cp\u003e(2015)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTactile Feedback\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUser Response Analysis\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1200 samples\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh (\u0026gt; 88%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLimited demographic range\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrachan \u0026amp; Panëels (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHaptic Gesture Authentication\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGesture-based with force feedback\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePilot study (N \u0026lt; 50)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFeasibility shown\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLimited scale; sensitivity to posture and lighting\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWazid et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTactile Internet Surgery\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSecure mutual authentication \u0026amp; session key security\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTI-enabled surgical setup\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eImproved encryption reliability\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLatency \u0026amp; real-time feedback challenge\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYan et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHaptic Passwords\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMODWT-based feature extraction \u0026amp; adaptive template scheme\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 subjects\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh authentication accuracy\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eForgery-proof performance, but limited sample size\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiu \u0026amp; Szirányi (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGesture Recognition\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYOLOv3-tiny \u0026amp; OpenPose\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUAV rescue dataset\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBody: 99.8%, Hand: 94.71%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEnvironmental limitations, Gesture clarity\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgrawal \u0026amp; Sharma (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTouch Dynamics\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKeystroke and swipe-based behavioral biometrics\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate sample (N ≈ 100)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e~ 89% accuracy\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLimited to Android; small demographic base\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeorgiev et al.\u003c/p\u003e \u003cp\u003e(2022)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContinuous Authentication\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMulti algorithm ensemble classifier \u0026amp; RNN stacking aggregation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThree Public Datasets\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOutperforms existing touch models\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBehavioral drift, device heterogeneity\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGattulli et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContinuous Authentication\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTouch Events \u0026amp; Human Activity Recognition\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH-MOG Dataset\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98.9% (1-class SVM)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLimited real-world testing\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrandi et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXR Authentication\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTime-Based OTPs, Haptics \u0026amp; Kinetic Activity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExperimental XR setup\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh authentication reliability\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eXR Device Compatibility\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLi et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVibHead Authentication\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCNN-based vibration pattern classification through smart headsets\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExperimental setup with Microsoft HoloLens\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh authentication accuracy (~ 95%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDevice compatibility \u0026amp; vibration consistency\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOlabanji et al.\u003c/p\u003e \u003cp\u003e(2024)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI-Driven IAM\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI-enhanced authentication \u0026amp; access control\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e582 cybersecurity experts surveyed\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh accuracy in cloud-based IAM\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHardware configurations \u0026amp; security concerns\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRehman \u0026amp; Ali (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIAM with AI Integration\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI-based Behavioral risk models\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEnterprise datasets (not disclosed)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh predictive accuracy\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eData dependency, regulatory concerns\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eHaptic authentication is transitioning from experimental proofs-of-concept to deployable authentication technologies. With the integration of AI, multimodal input, and continuous authentication, this domain is poised to redefine how users interact securely with devices. Moving forward, the focus must shift toward addressing standardization, cross-device performance, and data privacy to ensure practical, scalable deployment.\u003c/p\u003e"},{"header":"5. Conclusion and Future Directions","content":"\u003cp\u003eThe evolving threat landscape in cybersecurity has exposed vulnerabilities in traditional authentication systems, including passwords, PINs, and even certain biometric techniques like fingerprint recognition. As cyberattacks become more sophisticated, relying solely on static authentication measures is no longer sufficient. Haptic authentication offers a dynamic, behavior-based security solution that enhances protection in several critical ways.\u003c/p\u003e\u003cp\u003eThis systematic review paper in haptic authentication synthesizes multiple authentication modalities and addresses key gaps in prior studies. This review categorizes methodologies systematically, offering a comparative framework for evaluating effectiveness. It introduces a Multi-Factor Comparative Analysis and a quality-assessed comparison of state-of-the-art systems.\u003c/p\u003e\u003cp\u003eThis integrates data-driven comparisons of success rates, classification techniques, and authentication precision, ensuring empirical validation. Unlike earlier fragmented discussions, this work provides a structured evaluation of machine learning-driven authentication models. It also highlights challenges beyond technical feasibility: Previous studies focused primarily on algorithm accuracy, but this review expands the discussion to include hardware standardization, privacy implications, and environmental variability. This broader scope ensures practical applicability in real-world deployments.\u003c/p\u003e\u003cp\u003eThis review suggests next-generation AI-driven adaptive authentication frameworks. Future work should be in development of light-weight, context-aware haptic authentication systems to enable personalized authentication stategies for users.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgrawal R, Sharma P (2022) A study of touch dynamics biometrics authentication. \u003cem\u003eInternational Journal of Scientific Research in Engineering and Management (IJSREM)\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(7)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrank M, Biedert R, Ma E, Martinovic I, Song D (2012) Touchalytics: On the applicability of touchscreen input as a behavioral biometric for continuous authentication. IEEE Trans Inf Forensics Secur 8(1):136\u0026ndash;148\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGattulli V, Impedovo D, Pirlo G, Volpe F (2023) Touch events and human activities for continuous authentication via smartphone. Sci Rep 13(1):10515\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeorgiev M, Eberz S, Martinovic I (2022) Techniques for continuous touch-based authentication modeling. \u003cem\u003earXiv preprint arXiv:2207.12140\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrandi JG, Terrell J, Lofca K, Ruizvalencia C, Kopper R (2023), March A continuous authentication technique for XR utilizing time-based one time passwords, haptics, and kinetic activity. In \u003cem\u003e2023 IEEE conference on virtual reality and 3D user interfaces abstracts and workshops (VRW)\u003c/em\u003e (pp. 959\u0026ndash;960). IEEE\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKam YHS, Chin JJ (2022) Authentication Methods that use Haptic. A Review, and Audio\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaur K, Cook DM (2019) Haptic alternatives for mobile device authentication by older technology users. 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Springer International Publishing\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWazid M, Das AK, Lee JH (2019) User authentication in a tactile internet based remote surgery environment: Security issues, challenges, and future research directions. Pervasive Mob Comput 54:71\u0026ndash;85\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan J, Bonaci T, Chizeck HJ (2020) Your signature is your password: Haptic passwords on mobile devices. \u003cem\u003earXiv preprint arXiv:2010.14007\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Haptic Authentication, User Authentication, Biometrics, Tactile Feedback, Touch Dynamics","lastPublishedDoi":"10.21203/rs.3.rs-6543696/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6543696/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUser authentication is a critical component in securing digital environments, driven by the rapid advancements in the technological revolution. Haptic authentication has emerged as a promising paradigm in the field of user authentication, using tactile feedback and touch-based biometrics for secure and user-friendly access control. This paper presents a systematic literature review of recently published research papers on haptic authentication. This systematic review synthesizes recent advancements in haptic authentication, focusing on methodologies such as touch dynamics, force feedback, and vibrotactile systems. This paper explore their applications, and the challenges associated with their implementation. Challenges such as hardware standardization and data privacy are identified, alongside future opportunities in hybrid models and AI-driven personalization. Through an analysis of peer-reviewed articles, conference proceedings, and patent literature, this review provides a comprehensive overview of haptic authentication's potential to enhance security and discuss future research directions.\u003c/p\u003e","manuscriptTitle":"User Authentication Based on Haptic: A Systematic Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-29 10:46:31","doi":"10.21203/rs.3.rs-6543696/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d0fac603-2f35-4120-bce1-1c67452e4fca","owner":[],"postedDate":"April 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-29T10:46:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-29 10:46:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6543696","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6543696","identity":"rs-6543696","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}