{"paper_id":"318f2a2d-cc45-4dee-9aec-b3312f0b2d39","body_text":"EMG-Controlled Robotic Hand Prosthesis: Towards a Functional, Affordable, and Scalable Solution for Individuals with Amputations | 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 Research Article EMG-Controlled Robotic Hand Prosthesis: Towards a Functional, Affordable, and Scalable Solution for Individuals with Amputations Arturo Alejandro Diaz Ortiz, Medaly Eulogio Saenz, Diego Alexandro Padilla Llanca, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6778625/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 This article presents the design, development, and implementation of a robotic hand prosthesis controlled by electromyographic (EMG) signals, aimed at individuals with partial amputations. The proposed solution establishes an intuitive interface between the remaining forearm muscles and a robotic structure capable of replicating complex finger movements. The system incorporates surface EMG sensors, efficient signal processing algorithms, and cost-effective actuators based on servomotors. Performance is evaluated in terms of accuracy, response time, and robustness against electromagnetic noise, achieving a 90% fidelity in motion replication and response times under 100 ms. This proposal represents a functional, affordable, and scalable alternative with the potential to democratize access to high-functionality prostheses in low-resource settings. Biomedical Engineering Robotic prosthesis EMG control low-cost actuation signal processing biomedical engineering human–machine interface Figures Figure 1 Figure 2 Figure 3 Figure 4 I. INTRODUCTION Upper limb amputation, particularly of the hand, represents a multidimensional challenge that goes beyond the physical aspect, profoundly affecting individuals’ independence, psychological well-being, and social integration. According to recent estimates from the World Health Organization (WHO), nearly 30 million people worldwide live with some form of amputation, and a significant percentage of them lack access to prosthetic solutions due to high costs or unavailability in low-resource regions. The development of advanced robotic prostheses has shown great potential to restore both basic and complex hand functions. However, these solutions—typically developed by specialized companies—present insurmountable economic barriers for most potential users. The cost of a commercial myoelectric prosthesis can exceed USD 10,000, making it inaccessible in much of the developing world. On the other hand, simple mechanical prostheses, although more affordable, lack the functionality and intuitive control required for performing complex tasks, thereby limiting user independence. In this context, there is a clear need to develop affordable, modular, and customizable solutions that combine accessibility with advanced functionality. The system proposed in this work combines the best of these approaches by integrating low-cost components and robust signal processing techniques to deliver a functional and affordable solution. II. PROBLEM STATEMENT A. Contextualization The human hand is a complex organ that combines strength, precision, and sensitivity. The interactions between muscles, tendons, and bones allow for the execution of highly complex tasks such as writing, manipulating small objects, or performing expressive gestures. Main movements Flexion and extension: Controlled by extrinsic muscles located in the forearm. Adduction and abduction: Controlled by intrinsic muscles of the hand. Thumb rotation: A critical movement for object manipulation. Technical Challenges in Robotic Prostheses 1. EMG Signal Processing: Muscle signals are often contaminated with electrical noise, motion artifacts, and physiological interferences. Real-time processing requires robust and efficient algorithms. 2. Actuator Control in Prostheses: Actuators must replicate natural movements smoothly, avoiding abrupt or jerky responses. 3. Cost Optimization: Reducing costs without compromising functionality is essential for widespread adoption. III. METHODOLOGY The methodology adopted in this project follows an interdisciplinary approach that combines principles of biomedical engineering, electronics, mechanics, and programming. Below are the key steps described in detail for the design, implementation, and evaluation of the system. A. General System Architecture The system consists of three main modules: 1. Electromyographic (EMG) Signal Acquisition: Using MyoWare sensors, muscle signals generated by the activity of the forearm flexors and extensors are detected. 2. Signal Processing and Analysis: EMG data are filtered, rectified, and normalized in real-time using an Arduino microcontroller. 3. Mechanical Actuator Control: The processed values are converted into commands for servomotors that replicate finger movements. B. EMG Signal Acquisition EMG Sensor Configuration The MyoWare sensor was used due to its low cost and ease of integration. This sensor measures the electrical activity generated during muscle contraction and produces a proportional analog signal. Parameter Specification Input Voltage 3.3-5V Output Range 0-5V Sampling Frequency 1 kHz Electrode placement was crucial to ensure proper signal acquisition. Electrodes were positioned over the flexor digitorum superficialis and extensor digitorum communis muscles, following standard anatomical protocols. Common Issues During Acquisition 1. Interference noise: Caused by nearby electrical devices. 2. Motion artifacts: Introduced by the user’s non-muscular activity. C. Mechanical Design of the Prosthesis Materials Used The prosthesis was designed using PLA (polylactic acid), an affordable material that is easy to work with using 3D printers. Component Material Cost (USD) Manufacturing Method Articulated fingers PLA 15 3D printing Palm PLA 20 3D printing Servos (4) Plastic 40 Commercial Commercial CAD Design The design was created using Fusion 360. Each finger was connected to an SG90 servomotor, allowing for independent movements. Actuator Control The SG90 servos were controlled using PWM pulses. The angular position was adjusted proportionally to the contraction strength detected by the EMG sensors. · System Testing and Calibration Preliminary tests were carried out to calibrate: 1) EMG signal thresholds: Adjusted to detect both light and strong contractions. 2) Movement angles: The full range from 0° to 180° was tested. Movement Accuracy (%) Response Time (ms) Full Flexion 94 85 Partial Extension 91 80 Complex Movement 89 100 IV. DISCUSSION The results demonstrate that the system is a functional and economical alternative to high-cost commercial prostheses. The achieved accuracy and response time are competitive, even when compared to professional solutions. However, several areas for improvement were identified, such as: 1. Mechanical design optimization: Using more resistant materials could enhance the prosthesis's durability. 2. Advances in signal processing: Integrating machine learning algorithms could improve the accuracy of complex movements. 3. Personalized calibration: Adaptive systems for different muscle strength levels in users. Conclusions The design and development of an accessible and low-cost EMG-controlled hand prosthesis represents a significant advancement in prosthetic device technology. This project has successfully developed a functional solution that not only improves the quality of life for people with amputations but also offers a replicable model for implementation in low-resource communities. With a total cost under $150 USD, the proposed system represents a viable alternative to expensive commercial devices, which remain inaccessible to most of the global population. Nevertheless, challenges remain to be addressed, such as material optimization to improve durability and the integration of haptic feedback for a more natural experience. Despite these challenges, the future of low-cost prosthetics appears promising, and this work lays the foundation for a revolution in the accessibility and sustainability of prosthetic devices. Declarations Acknowledgments Our deepest gratitude goes to our parents, who were the main inspiration for bringing this research to life. We also thank our university, the National University of Engineering , for providing us with the guidance and knowledge that made this project possible. References Biddiss, E. A., & Chau, T. (2007). Upper-limb prosthetics: Critical factors in device end-user satisfaction. Journal of Rehabilitation Research and Development, 44(6), 831–844. MyoWare. (2024). MyoWare Muscle Sensor. Retrieved from https://www.advancertechnologies.com /myoware-muscle-sensor Ottobock, (2023). MyoHand VariPlus Speed. Ottobock Healthcare. Ho, J., & Lee, H. (2016). Low-Cost Bionic Prosthesis Control Using EMG Signal. IEEE Transactions on Robotics, 32(1), 112-122. Reh, R., et al. (2015). Sustainability and Environmental Impacts of 3D Printed Prostheses. Journal of Sustainable Manufacturing, 29(5), 354–365. Saucier, G. (2019). Future Trends in Prosthetics: Enhancing Mobility with Intelligent Devices. Science and Technology Review, 42(7), 150–156. 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-6778625\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":463785345,\"identity\":\"3d1c325a-6f4c-44ae-9b63-b21ed7f1a118\",\"order_by\":0,\"name\":\"Arturo Alejandro Diaz 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INTRODUCTION\",\"content\":\"\\u003cp\\u003eUpper limb amputation, particularly of the hand, represents a multidimensional challenge that goes beyond the physical aspect, profoundly affecting individuals\\u0026rsquo; independence, psychological well-being, and social integration. According to recent estimates from the World Health Organization (WHO), nearly 30 million people worldwide live with some form of amputation, and a significant percentage of them lack access to prosthetic solutions due to high costs or unavailability in low-resource regions.\\u003c/p\\u003e\\n\\u003cp\\u003eThe development of advanced robotic prostheses has shown great potential to restore both basic and complex hand functions. However, these solutions\\u0026mdash;typically developed by specialized companies\\u0026mdash;present insurmountable economic barriers for most potential users. The cost of a commercial myoelectric prosthesis can exceed USD 10,000, making it inaccessible in much of the developing world.\\u003c/p\\u003e\\n\\u003cp\\u003eOn the other hand, simple mechanical prostheses, although more affordable, lack the functionality and intuitive control required for performing complex tasks, thereby limiting user independence. In this context, there is a clear need to develop affordable, modular, and customizable solutions that combine accessibility with advanced functionality. The system proposed in this work combines the best of these approaches by integrating low-cost components and robust signal processing techniques to deliver a functional and affordable solution.\\u003c/p\\u003e\"},{\"header\":\"II. PROBLEM STATEMENT\",\"content\":\"\\u003ch4\\u003eA. \\u0026nbsp;Contextualization\\u003c/h4\\u003e\\n\\u003cp\\u003eThe human hand is a complex organ that combines strength, precision, and sensitivity. The interactions between muscles, tendons, and bones allow for the execution of highly complex tasks such as writing, manipulating small objects, or performing expressive gestures.\\u003c/p\\u003e\\n\\u003cp\\u003eMain movements\\u003c/p\\u003e\\n\\u003cul\\u003e\\n \\u003cli\\u003eFlexion and extension: Controlled by extrinsic muscles located in the forearm.\\u003c/li\\u003e\\n \\u003cli\\u003eAdduction and abduction: Controlled by intrinsic muscles of the hand.\\u003c/li\\u003e\\n \\u003cli\\u003eThumb rotation: A critical movement for object manipulation.\\u003c/li\\u003e\\n\\u003c/ul\\u003e\\n\\u003cp\\u003eTechnical Challenges in Robotic Prostheses\\u003c/p\\u003e\\n\\u003cp\\u003e1. EMG Signal Processing: Muscle signals are often contaminated with electrical noise, motion artifacts, and physiological interferences. Real-time processing requires robust and efficient algorithms.\\u003c/p\\u003e\\n\\u003cp\\u003e2. Actuator Control in Prostheses:\\u003cbr\\u003eActuators must replicate natural movements smoothly, avoiding abrupt or jerky responses.\\u003c/p\\u003e\\n\\u003cp\\u003e3. Cost Optimization:\\u003cbr\\u003eReducing costs without compromising functionality is essential for widespread adoption.\\u003c/p\\u003e\"},{\"header\":\"III. METHODOLOGY\",\"content\":\"\\u003cp\\u003eThe methodology adopted in this project follows an interdisciplinary approach that combines principles of biomedical engineering, electronics, mechanics, and programming. Below are the key steps described in detail for the design, implementation, and evaluation of the system.\\u003c/p\\u003e\\n\\u003ch3\\u003eA. General System Architecture\\u003c/h3\\u003e\\n\\u003cp\\u003eThe system consists of three main modules:\\u003c/p\\u003e\\n\\u003cp\\u003e1. \\u003cstrong\\u003eElectromyographic (EMG) Signal Acquisition:\\u003c/strong\\u003e Using MyoWare sensors, muscle signals generated by the activity of the forearm flexors and extensors are detected.\\u003c/p\\u003e\\n\\u003cp\\u003e2. \\u003cstrong\\u003eSignal Processing and Analysis:\\u003c/strong\\u003e EMG data are filtered, rectified, and normalized in real-time using an Arduino microcontroller.\\u003c/p\\u003e\\n\\u003cp\\u003e3. \\u003cstrong\\u003eMechanical Actuator Control:\\u003c/strong\\u003e The processed values are converted into commands for servomotors that replicate finger movements.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eB. EMG Signal Acquisition\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eEMG Sensor Configuration\\u003c/p\\u003e\\n\\u003cp\\u003eThe MyoWare sensor was used due to its low cost and ease of integration. This sensor measures the electrical activity generated during muscle contraction and produces a proportional analog signal.\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eParameter\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 92px;\\\"\\u003e\\n \\u003cp\\u003eSpecification\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eInput Voltage\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 92px;\\\"\\u003e\\n \\u003cp\\u003e3.3-5V\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eOutput Range\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 92px;\\\"\\u003e\\n \\u003cp\\u003e0-5V\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 141px;\\\"\\u003e\\n \\u003cp\\u003eSampling Frequency\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 92px;\\\"\\u003e\\n \\u003cp\\u003e1 kHz\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eElectrode placement was crucial to ensure proper signal acquisition. Electrodes were positioned over the flexor digitorum superficialis and extensor digitorum communis muscles, following standard anatomical protocols.\\u003c/p\\u003e\\n\\u003cp\\u003eCommon Issues During Acquisition\\u003c/p\\u003e\\n\\u003cp\\u003e1. Interference noise: Caused by nearby electrical devices.\\u003c/p\\u003e\\n\\u003cp\\u003e2. Motion artifacts: Introduced by the user\\u0026rsquo;s non-muscular activity.\\u003c/p\\u003e\\n\\u003ch3\\u003eC. Mechanical Design of the Prosthesis\\u003c/h3\\u003e\\n\\u003cp\\u003eMaterials Used\\u003c/p\\u003e\\n\\u003cp\\u003eThe prosthesis was designed using PLA (polylactic acid), an affordable material that is easy to work with using 3D printers.\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"257\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 80px;\\\"\\u003e\\n \\u003cp\\u003eComponent\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 57px;\\\"\\u003e\\n \\u003cp\\u003eMaterial\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 52px;\\\"\\u003e\\n \\u003cp\\u003eCost (USD)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003eManufacturing Method\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 80px;\\\"\\u003e\\n \\u003cp\\u003eArticulated fingers\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 57px;\\\"\\u003e\\n \\u003cp\\u003ePLA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 52px;\\\"\\u003e\\n \\u003cp\\u003e15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003e3D printing\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 80px;\\\"\\u003e\\n \\u003cp\\u003ePalm\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 57px;\\\"\\u003e\\n \\u003cp\\u003ePLA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 52px;\\\"\\u003e\\n \\u003cp\\u003e20\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003e3D printing\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 80px;\\\"\\u003e\\n \\u003cp\\u003eServos (4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 57px;\\\"\\u003e\\n \\u003cp\\u003ePlastic\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 52px;\\\"\\u003e\\n \\u003cp\\u003e40\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003eCommercial\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eCommercial CAD Design\\u003c/p\\u003e\\n\\u003cp\\u003eThe design was created using Fusion 360. Each finger was connected to an SG90 servomotor, allowing for independent movements.\\u003c/p\\u003e\\n\\u003cp\\u003eActuator Control\\u003c/p\\u003e\\n\\u003cp\\u003eThe SG90 servos were controlled using PWM pulses. The angular position was adjusted proportionally to the contraction strength detected by the EMG sensors.\\u003c/p\\u003e\\n\\u003ch3\\u003e\\u0026middot; System Testing and Calibration\\u003c/h3\\u003e\\n\\u003cp\\u003ePreliminary tests were carried out to calibrate:\\u003c/p\\u003e\\n\\u003cp\\u003e1) EMG signal thresholds: Adjusted to detect both light and strong contractions.\\u003c/p\\u003e\\n\\u003cp\\u003e2) Movement angles: The full range from 0\\u0026deg; to 180\\u0026deg; was tested.\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"258\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003eMovement\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003eAccuracy (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 91px;\\\"\\u003e\\n \\u003cp\\u003eResponse Time (ms)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003eFull Flexion\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e94\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 91px;\\\"\\u003e\\n \\u003cp\\u003e85\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003ePartial Extension\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e91\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 91px;\\\"\\u003e\\n \\u003cp\\u003e80\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 96px;\\\"\\u003e\\n \\u003cp\\u003eComplex Movement\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 71px;\\\"\\u003e\\n \\u003cp\\u003e89\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 91px;\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\"},{\"header\":\"IV. DISCUSSION\",\"content\":\"\\u003cp\\u003eThe results demonstrate that the system is a functional and economical alternative to high-cost commercial prostheses. The achieved accuracy and response time are competitive, even when compared to professional solutions. However, several areas for improvement were identified, such as:\\u003c/p\\u003e\\n\\u003cp\\u003e1. Mechanical design optimization: Using more resistant materials could enhance the prosthesis's durability.\\u003c/p\\u003e\\n\\u003cp\\u003e2. Advances in signal processing: Integrating machine learning algorithms could improve the accuracy of complex movements.\\u003c/p\\u003e\\n\\u003cp\\u003e3. Personalized calibration: Adaptive systems for different muscle strength levels in users.\\u003c/p\\u003e\\n\\n\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eThe design and development of an accessible and low-cost EMG-controlled hand prosthesis represents a significant advancement in prosthetic device technology. This project has successfully developed a functional solution that not only improves the quality of life for people with amputations but also offers a replicable model for implementation in low-resource communities.\\u003cbr\\u003e With a total cost under $150 USD, the proposed system represents a viable alternative to expensive commercial devices, which remain inaccessible to most of the global population. Nevertheless, challenges remain to be addressed, such as material optimization to improve durability and the integration of haptic feedback for a more natural experience.\\u003cbr\\u003e Despite these challenges, the future of low-cost prosthetics appears promising, and this work lays the foundation for a revolution in the accessibility and sustainability of prosthetic devices.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eAcknowledgments\\u003c/p\\u003e\\n\\u003cp\\u003eOur deepest gratitude goes to our parents, who were the main inspiration for bringing this research to life. We also thank our university, the \\u003cem\\u003eNational University of Engineering\\u003c/em\\u003e, for providing us with the guidance and knowledge that made this project possible.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eBiddiss, E. A., \\u0026amp; Chau, T. (2007). Upper-limb prosthetics: Critical factors in device end-user satisfaction. Journal of Rehabilitation Research and Development, 44(6), 831\\u0026ndash;844. \\u003c/li\\u003e\\n\\u003cli\\u003eMyoWare. (2024). MyoWare Muscle Sensor. Retrieved from https://www.advancertechnologies.com /myoware-muscle-sensor\\u003c/li\\u003e\\n\\u003cli\\u003eOttobock, (2023). MyoHand VariPlus Speed. Ottobock Healthcare.\\u003c/li\\u003e\\n\\u003cli\\u003eHo, J., \\u0026amp; Lee, H. (2016). Low-Cost Bionic Prosthesis Control Using EMG Signal. IEEE Transactions on Robotics, 32(1), 112-122.\\u003c/li\\u003e\\n\\u003cli\\u003eReh, R., et al. (2015). Sustainability and Environmental Impacts of 3D Printed Prostheses. Journal of Sustainable Manufacturing, 29(5), 354\\u0026ndash;365.\\u003c/li\\u003e\\n\\u003cli\\u003eSaucier, G. (2019). Future Trends in Prosthetics: Enhancing Mobility with Intelligent Devices. Science and Technology Review, 42(7), 150\\u0026ndash;156.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"universidad nacional de Ingeniería\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"Robotic prosthesis, EMG control, low-cost actuation, signal processing, biomedical engineering, human–machine interface\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6778625/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6778625/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThis article presents the design, development, and implementation of a robotic hand prosthesis controlled by electromyographic (EMG) signals, aimed at individuals with partial amputations. The proposed solution establishes an intuitive interface between the remaining forearm muscles and a robotic structure capable of replicating complex finger movements. The system incorporates surface EMG sensors, efficient signal processing algorithms, and cost-effective actuators based on servomotors. Performance is evaluated in terms of accuracy, response time, and robustness against electromagnetic noise, achieving a 90% fidelity in motion replication and response times under 100 ms. This proposal represents a functional, affordable, and scalable alternative with the potential to democratize access to high-functionality prostheses in low-resource settings.\\u003c/p\\u003e\",\"manuscriptTitle\":\"EMG-Controlled Robotic Hand Prosthesis: Towards a Functional, Affordable, and Scalable Solution for Individuals with Amputations\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-05-30 10:10:57\",\"doi\":\"10.21203/rs.3.rs-6778625/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"0e272c1f-1656-4640-a16a-0f1e6d8a6cbf\",\"owner\":[],\"postedDate\":\"May 30th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":49280497,\"name\":\"Biomedical Engineering\"}],\"tags\":[],\"updatedAt\":\"2025-05-30T10:10:57+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-05-30 10:10:57\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6778625\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6778625\",\"identity\":\"rs-6778625\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}