Enhancing Musculoskeletal Healthcare through Mobile Application-Integrated Infrared Thermography: A Promising Approach for Diagnosis and Monitoring

Enhancing Musculoskeletal Healthcare through Mobile Application-Integrated Infrared Thermography: A Promising Approach for Diagnosis and Monitoring | 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 Enhancing Musculoskeletal Healthcare through Mobile Application-Integrated Infrared Thermography: A Promising Approach for Diagnosis and Monitoring MOHAMMED ABOO SALEH MOHAMMED ABOO SALEH This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4306725/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 Musculoskeletal injuries, encompassing conditions affecting muscles, bones, ligaments, tendons, and joints, pose significant diagnostic and treatment challenges, often requiring subjective assessments by healthcare providers. In this study, an innovative approach was employed using infrared imaging technology integrated with a mobile application to enhance objective assessment, particularly during massage therapy. Thermal imaging captures of patients' backs during pre- and post-therapy sessions were conducted via the developed app to identify abnormal temperature patterns indicative of underlying conditions. This novel method of diagnosis offers a departure from traditional subjective assessments by providing quantitative thermal data for analysis. The utilization of deep learning algorithms and thermal image processing techniques further facilitated data management and analysis, offering a more precise diagnostic approach. The application's diagnostic capabilities, coupled with its ability to streamline data management, distinguish it from conventional diagnostic methods, offering potential advancements in musculoskeletal injury diagnosis and treatment. This paper elucidates the methods employed, highlights the observed results, underscores the potential of integrating advanced technologies for musculoskeletal injury diagnosis and treatment, and provides a brief overview of musculoskeletal injuries to contextualize the study. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. INTRODUCTION Musculoskeletal injuries encompass a broad spectrum of conditions affecting the muscles, bones, ligaments, tendons, and joints, often arising from trauma, repetitive strain, or underlying health issues. Musculoskeletal pain is frequently characterized in words that imply a specific cause or precise site of tissue injury ( 1 ). The diagnosis and treatment of these injuries present significant challenges, typically relying on subjective evaluations by healthcare providers. Medical applications already implemented in microscope/imaging systems have been developed as a platform for diagnosis at POC (Point Of Care)( 2 ), infrared thermography, and phone-based oxygen level estimation system( 3 ). Medical applications may also assist clinical. professionals with time management, patient data access, communication, consultation, patient monitoring, and medical training ( 4 ). HemaApp( 1 ) from the University of Washington, for example, uses a sophisticated machine-learning approach to detect hemoglobin concentration using the phone's camera by employing external light sources such as an incandescent light bulb or a set of LEDs. However, these extra light sources are not widely available, and their use is limited in newer smartphones with an IR filter built into the camera lens ( 2 ). Traditional diagnostic methods, such as physical examinations and imaging studies like X-rays or MRIs, have limitations in providing real-time, quantitative data on the physiological changes occurring within the body. Recent advancements in medical imaging have sparked interest in the use of infrared thermography for assessing musculoskeletal injuries. The treatment methods discussed here can also be used for non-specific musculoskeletal problems in general ( 5 ). While pain may appear to be non-specific from a musculoskeletal aspect, it is critical to recognize and evaluate particular processes such as central sensitization( 6 ). Infrared thermography is a non-invasive imaging technique that detects thermal patterns on the body's surface by measuring infrared radiation emitted by the skin. Changes in skin temperature can signify underlying physiological alterations, including inflammation, muscle tension, or alterations in blood circulation. As such, thermal imaging holds promise as a valuable tool for objectively assessing musculoskeletal injuries. This study aims to explore the integration of infrared thermography technology with mobile applications to enhance the objective assessment of musculoskeletal injuries, particularly in the context of massage therapy. The average normal body temperature is 37°C and a few degrees Celsius difference to this range is considered as physiologically abnormal ( 7 ). In most cases, a raised temperature in different regions of the body is an indication of inflammation, tension, lack of blood circulation, etc( 7 ). By capturing thermal images of patients' backs before and after therapy sessions, we seek to identify abnormal temperature patterns indicative of underlying conditions. Medical Infrared thermography (MIT) is a promising solution to measure the body temperature across a large area or the full body surface ( 8 ) The development of a mobile application facilitates data management and analysis, offering a novel approach to musculoskeletal injury diagnosis and treatment. In this research, we have developed a mobile application for the diagnosis of musculoskeletal injuries, wherein an infrared camera is connected to a mobile device. This integration allows clinicians to capture thermal images of patients' bodies using the mobile application, facilitating non-invasive and efficient assessment of musculoskeletal health. Through the mobile application, clinicians can easily access and analyze thermal images captured by the infrared camera, enabling the detection of abnormal temperature patterns associated with musculoskeletal conditions. The connectivity between the infrared camera and the mobile device represents a significant advancement in the field of musculoskeletal medicine, offering clinicians a convenient and accessible tool for diagnosing and monitoring patients' conditions in real time. By leveraging infrared technology and mobile platforms, our research aims to improve the diagnosis and treatment of individuals with musculoskeletal issues, ultimately enhancing patient outcomes and quality of care. 2. Literature Review 2.1. Musculoskeletal: The musculoskeletal system encompasses bones, muscles, joints, and related tissues like cartilage, ligaments, tendons, and bursae. It supports both stability and movement in the body. Common complaints include neck pain, limb pain, low back pain, joint pain, and generalized discomfort, often leading to primary care visits. Low back pain, being prevalent, serves as an example due to its extensive treatment evidence. Treatments discussed here are applicable to various musculoskeletal issues. While pain may seem non-specific, it's essential to address specific processes like central sensitization. 2.2. Clinical Treatment: Clinical therapy for musculoskeletal pain comprises pharmacological and non-pharmacological approaches. Pharmacological treatments, including the WHO analgesic ladder, are less effective for non-specific, chronic, or widespread pain. Tricyclic antidepressants like amitriptyline show efficacy in neuropathic pain associated with conditions like fibromyalgia and low back pain. Non-pharmacological interventions, such as physical exercise and cognitive patient education, are cornerstone treatments, ideally delivered together as part of a multidisciplinary rehabilitation program. Various physical activities, from walking to swimming, have shown to reduce pain and improve function in musculoskeletal disorders. Close monitoring of pain and adverse effects is essential when using analgesics for chronic musculoskeletal conditions. ( 1 , 9 ). 2.3. Thermography: Thermography, utilizing infrared cameras, gauges surface temperature distribution to assess underlying structural issues or behaviour. It comes in two forms: passive and active. Passive thermography identifies temperature changes by heating the surface, revealing anomalies that may indicate underlying concerns. Active thermography, on the other hand, directly measures item temperature to pinpoint damage. Its applications span various fields such as agriculture, industry, medicine, and veterinary practice ( 10 ). 2.4. Deep Learning: Deep learning, a subset of machine learning, involves learning data representations through layers in a Deep Neural Network (DNN). Each layer transforms input data into increasingly abstract representations, culminating in predictions ( 11 , 12 ). This architecture finds applications in musculoskeletal imaging, such as tissue segmentation, image reconstruction, and illness identification ( 13 ). Inspired by the brain's interconnected neurons, DNNs consist of input, hidden, and output layers. Convolutional Neural Networks (CNNs), prevalent in medical imaging, feature convolutional and pooling layers ( 12 , 14 ). Activation functions ensure only relevant features pass between layers. Typically trained with supervised learning using paired images and labels, CNNs are evaluated for diagnostic performance post-training. 2.4. Conclusion: In this literature review we discussed every related paper with the study. But when we concentrated on the specifically musculoskeletal pain, we didn’t find any valid context for reference. For example, as mentioned earlier HemaApp ( 3 , 15 )from the University of Washington, uses a sophisticated machine learning approach to detect hemoglobin concentration using the phone's camera by employing external light sources. That’s where this research is valuable for finding the pains and provide a new solution to the world. This study goal is to enhance the diagnosis and treatment of individuals with musculoskeletal issues by combining infrared mobile technology with a mobile application. According to the findings, the benefit of this technique is to track and provide a proper solution for the clients. 3. FUTURE WORKS Future research in this field could focus on optimizing deep learning algorithms for more accurate detection of thermal anomalies, conducting longitudinal studies to assess long-term treatment outcomes, and developing standardized protocols for thermal imaging in musculoskeletal assessments. Integrating additional sensor data and expanding research to include larger patient populations would further enhance the utility and applicability of thermal imaging technology in clinical practice. Future work can also focus on enhancing the mobile application's functionality and user experience, integrating features for real-time data analysis, automated report generation, and secure cloud storage. Interactive elements like educational resources and exercise programs could also be incorporated to empower patient engagement in rehabilitation. Exploring telemedicine and remote monitoring opportunities would extend the application's reach beyond clinical settings, enabling healthcare providers to assess and monitor patients' musculoskeletal health remotely. Collaboration with software developers and human-computer interaction experts would ensure the application meets diverse needs, enhancing its usability and impact in musculoskeletal healthcare. 4. CONCLUTION the integration of mobile applications with infrared thermography presents a promising avenue for enhancing musculoskeletal healthcare. The developed mobile application not only enables real-time thermal imaging but also provides clinicians with convenient access to patient data, facilitating efficient monitoring and analysis. Future research could explore further enhancements to the mobile application interface, such as incorporating features for data visualization, patient management, and treatment tracking. Additionally, efforts to optimize the application for compatibility with different smartphone models and operating systems would broaden its accessibility and usability in clinical settings. By leveraging the capabilities of mobile technology, infrared thermography can be seamlessly integrated into routine clinical practice, empowering healthcare professionals with valuable tools for diagnosing and managing musculoskeletal conditions effectively. 5. MATERIALS AND METHODS 5.1. Hardware Setup : The first phase of the hardware setup involved procuring a FLIR One infrared camera, meticulously selected for its seamless integration with smartphones and its exceptional high-resolution thermal imaging capabilities. The FLIR One camera, renowned for its compact design and portability, proved to be an ideal choice for capturing detailed thermal images of participants' musculoskeletal regions. With its advanced infrared sensor technology and precise thermal sensitivity, the camera ensured accurate detection and visualization of temperature variations on the body's surface. The compatibility of the FLIR One camera with smartphones facilitated convenient image capture and real-time analysis through dedicated mobile applications, enhancing the efficiency and accessibility of the thermal imaging process. The images were obtained in a room with a temperature of about 20ºC (68°F). The camera was positioned at a height of 150 cm and a distance of 2 m away from the patient to capture a full-body image. This carefully selected hardware played a pivotal role in enabling comprehensive data collection and analysis for the study's objectives. 5.2. Software Setup: The software setup commenced with the utilization of MATLAB, a multifaceted programming environment renowned for its extensive capabilities in scientific computing and algorithm development. MATLAB served as the primary platform for crafting sophisticated algorithms tailored to image processing, analysis, and visualization tasks. Leveraging MATLAB's comprehensive toolboxes and libraries, including the Image Processing Toolbox and Computer Vision Toolbox, facilitated the creation of robust algorithms optimized for thermal image analysis. These algorithms were meticulously designed to enhance the accuracy of temperature measurements, identify thermal anomalies, and extract valuable insights from the captured thermal images. In tandem with MATLAB, Android Studio, a powerful integrated development environment (IDE) specifically tailored for Android application development, was employed to create a custom mobile application. This application was purpose-built to seamlessly integrate with the FLIR One infrared camera and smartphones, enabling efficient thermal image capture, storage, and analysis directly on mobile devices. By harnessing the capabilities of Android Studio, developers were able to design an intuitive and user-friendly interface for the mobile application, empowering users to capture thermal images effortlessly and conduct real-time analysis of musculoskeletal regions. The synergy between MATLAB and Android Studio facilitated a seamless workflow, enabling researchers to leverage advanced image processing algorithms within a mobile environment for enhanced data collection and analysis capabilities. 5.3. Data Collection: The data collection phase commenced with the recruitment of volunteers falling within the specified age range of 25 to 40 years, ensuring a diverse representation of participants for the study. Rigorous screening procedures were implemented to select individuals without pre-existing musculoskeletal conditions or contraindications that could potentially confound the study results. The data collected can be uploaded and stored in the mobile application for future implementation and patient records as shown in Fig. 2 5.4. Therapy Sessions: Participants underwent targeted traditional massage therapy aimed at musculoskeletal regions with thermal anomalies. Following therapy, a 15-minute rest period was observed to stabilize physiological responses. Post-therapy thermal imaging immediately followed the rest period to assess changes in temperature distribution. This synchronized approach enabled real-time evaluation of treatment effectiveness, providing valuable insights into the impact of massage therapy on thermal abnormalities. By allowing time for physiological equilibrium and minimizing potential confounding factors, the rest period ensured accurate thermal imaging results. Patients with persistent musculoskeletal pain disorders, such as fibromyalgia and low back pain, have discovered that tricyclic antidepressants (TCA), particularly amitryptiline, are effective in treating neuropathic pain( 10 ). Physical exercise and patient education with a cognitive approach are the pillars of non-pharmacological treatment, which are best delivered in tandem as part of a multi-professional rehabilitation program( 10 ). The difference between the image analysis before and after massage therapy is aligned in the Table 1 The therapy sessions were conducted in a controlled environment, with careful temperature regulation to optimize imaging conditions. Licensed massage therapists administered the massages, utilizing manual manipulation techniques to alleviate tension and improve circulation in targeted areas. Throughout the sessions, participant comfort and safety were prioritized, with therapists adjusting techniques as needed to accommodate individual preferences. Feedback from participants was encouraged to ensure treatment effectiveness and address any concerns. The integration of thermal imaging with massage therapy allowed for comprehensive assessment and monitoring of musculoskeletal conditions. This holistic approach provides a promising avenue for enhancing diagnosis and treatment outcomes in individuals with thermal anomalies indicative of musculoskeletal issues. The complete musculoskeletal analysis is represented in the flow diagram in Fig. 1 5.5. Image Analysis Custom algorithms were meticulously developed in MATLAB to automate the analysis of thermal images obtained before and after therapy sessions. These algorithms were specifically designed to enhance the identification of thermal anomalies and quantify temperature changes in musculoskeletal regions. Sophisticated image processing techniques were employed to precisely locate and segment the areas of interest within the thermal images. The thermal palette is a way to represent temperature variations using color. Infrared cameras typically use various Thermal palettes where white often represents higher temperature and black color represents lower temperature Initially as shown in Fig. 4 the images were pre-processed to enhance contrast and reduce noise, ensuring optimal clarity for subsequent analysis. Subsequently, the musculoskeletal regions were delineated using advanced segmentation algorithms, which effectively separated the target areas from surrounding tissues or background. This segmentation process involved the application of thresholding, edge detection, and morphological operations to accurately define the boundaries of the regions displaying thermal anomalies. Once the regions of interest were identified, the algorithms extracted temperature data from the thermal images for quantitative analysis. Temperature values were measured within these regions both before and after therapy, allowing for direct comparison and assessment of temperature changes over time. Statistical methods, such as mean temperature calculation and temperature distribution analysis, were employed to quantify the extent of thermal alterations associated with the therapy. Additionally, heat maps and temperature profiles were generated to visualize the spatial distribution of temperature changes across the musculoskeletal regions. Overall, the developed algorithms provided robust tools for objective assessment and interpretation of thermal imaging data, facilitating comprehensive analysis of the therapeutic effects on musculoskeletal conditions. 5.6. Statistical Analysis Utilizing MATLAB, a histogram analysis was employed to assess the significance of temperature changes observed between the pre and post-therapy images as shown in Fig. 3.1 and Fig. 3.2. Histograms were generated to visualize the distribution of temperature values within the identified regions of interest before and after therapy. Thermography assesses the structural damage or behavior of what is under the surface by measuring the distribution of surface temperature( 5 ) By plotting the frequency of temperature values against temperature bins, the histograms provided an intuitive representation of the temperature distribution and any shifts in temperature patterns following therapy. Statistical tests, such as paired t-tests or non-parametric Wilcoxon signed-rank tests, can be conducted using the temperature data extracted from the histograms to determine histogram the significance of temperature changes. These tests evaluated whether there were statistically significant differences in temperature distributions between the pre and post-therapy images. The results of the statistical tests were interpreted to assess the efficacy of massage therapy in modulating thermal anomalies. Additionally, effect size measures, such as Cohen's d or eta-squared, may have been calculated based on the histogram data to quantify the magnitude of temperature changes observed between the pre and post-therapy conditions. These effect size measures provided insights into the practical significance of the observed temperature alterations, further informing the evaluation of therapy effectiveness. Overall, the combination of histogram analysis and statistical tests in MATLAB offered a comprehensive approach to assessing the significance of temperature changes before and after therapy. This methodology provided quantitative evidence supporting the efficacy of massage therapy in mitigating thermal anomalies associated with musculoskeletal conditions. 5.7. Ethical Considerations Ethical considerations were diligently managed throughout the research endeavor, commencing with Institutional Review Board (IRB) approval to uphold human research standards. Informed consent procedures were meticulously followed, ensuring participants were fully informed about the study's objectives, procedures, and potential risks before their involvement. Robust privacy protocols were implemented to maintain confidentiality, with participant identities anonymized to protect their privacy. Continuous monitoring and oversight mechanisms were in place to promptly address any ethical concerns or unforeseen issues that arose during the study. Adherence to the Declaration of Helsinki and institutional guidelines was paramount in upholding participant welfare and research integrity. Additionally, stringent measures were enacted to mitigate any potential discomfort or distress experienced by participants, prioritizing their well-being throughout the research process. Table.1. In this table summarizes the key observations and clinical significance of thermal imaging before and after therapy in individuals with and without musculoskeletal issues. Aspects Aspects Before Therapy After Therapy Healthy participants Relatively uniform temperature distributions Consistent thermal profiles, minimal temperature alterations Participants with MSK issues Localized temperature variations, indicative of inflammation, tension, or circulation issues Reduction in localized temperature anomalies, indicating therapeutic efficacy Observations Baseline measurements providing insights into physiological status Immediate physiological responses to therapeutic interventions Clinical Significants Understanding baseline thermal profiles for healthy individuals Assessing treatment effectiveness and guiding therapeutic decisions Utilizations Monitoring patient progress and optimizing therapy protocols Tracking treatment outcomes and guiding patient care strategies Declarations ACKNOWLEDGMENT To my first supervisor, Jean Ngoie who gave me the idea to work on this great research subject that I have always been interested in, thank you for your ultimate support and feedback, thank you for the regular chats which made me feel comfortable doing this work, you were a mentor and a dear friend. My Special thanks to the physiotherapist Catherine Louise Murrison who gave me support in the massage area of this research I would also like to show my gratitude to the people whom I sought for statistical analysis, Chunhui Li who have helped me a lot in my statistical work where I was completely lost. I would not have gotten any results without your guidance and wisdom. Finally, I would offer my sincere thanks to Scott McGregor from the University of Dundee Library and Learning Centre, who has helped me develop and structure my dissertation, you have saved no effort in helping, you are the kindest. Ethical Approval In the research described, ethical considerations were paramount throughout the study's duration. The following details can be added to address ethical approval: Institutional Review Board (IRB) Approval: Before commencing the study, approval from the Institutional Review Board was obtained to ensure compliance with ethical standards for human research. The IRB review process involved a thorough evaluation of the study protocol, including participant recruitment procedures, informed consent processes, data handling, and potential risks to participants. Informed Consent Procedures: Rigorous informed consent procedures were implemented to ensure that participants were fully informed about the study's objectives, procedures, and potential risks before their involvement. Participants were provided with detailed information about the study, including its purpose, procedures, potential benefits, and any foreseeable risks or discomforts. They were given adequate time to review the consent form and ask questions before voluntarily agreeing to participate. Participant Privacy and Confidentiality: Robust privacy protocols were implemented to safeguard participant confidentiality and privacy. Participant identities were anonymized to protect their privacy, and all data collected during the study were stored securely and accessed only by authorized personnel. Any personal information collected was kept confidential and used solely for research purposes. Continuous Monitoring and Oversight: Continuous monitoring and oversight mechanisms were in place throughout the study to promptly address any ethical concerns or unforeseen issues that arose. Researchers remained vigilant in monitoring participant welfare and research integrity, promptly addressing any ethical issues or deviations from the approved protocol. Adherence to Ethical Guidelines: Adherence to ethical guidelines, including the Declaration of Helsinki and institutional guidelines, was paramount in upholding participant welfare and research integrity. Researchers ensured that all aspects of the study were conducted in accordance with ethical principles, including respect for participant autonomy, beneficence, non-maleficence, and justice. By adhering to these ethical considerations and obtaining proper approval from the Institutional Review Board, the researchers ensured that the study was conducted ethically and with utmost regard for participant welfare and research integrity. Funding Not Applicable. Availability of data and materials To access the datasets used in this study, interested parties can request access through the corresponding author. Due to privacy and ethical considerations, access to the datasets may be subject to approval by the Institutional Review Board (IRB) or other relevant regulatory bodies. Additionally, data sharing may require compliance with data protection regulations and the completion of data use agreements. For inquiries regarding access to the datasets, please contact the corresponding author at [email protected] . Requests will be reviewed and processed in accordance with the applicable regulations and ethical guidelines governing data sharing and protection. Conflict of Interest: The authors declare that there are no conflicts of interest regarding the publication of this research. References Bergman S. Management of musculoskeletal pain. Best Practice & Research Clinical Rheumatology. 2007;21(1):153-66. Huang W, Luo S, Yang D, Zhang S. 基于智能手机的近红外成像, 测量和光谱技术在护理点诊断中的应用. Journal of Zhejiang University-SCIENCE B. 2021;22:171-89. Bui N, Nguyen A, Nguyen P, Truong H, Ashok A, Dinh T, et al., editors. Pho2: Smartphone based blood oxygen level measurement systems using near-ir and red wave-guided light. Proceedings of the 15th ACM conference on embedded network sensor systems; 2017. Green MS, Mathew JJ, Gundigi Venkatesh A, Green P, Tariq R. Utilization of smartphone applications by anesthesia providers. Anesthesiology Research and Practice. 2018;2018. Main C. de C, Williams AC. Musculoskeletal pain. Br Med J. 2002;325:534-7. Woolf CJ. Pain: moving from symptom control toward mechanism-specific pharmacologic management. Annals of internal medicine. 2004;140(6):441-51. Faerber J, Ngoie J. Integration of a Mobile Application using Medical Infrared Imaging to improve the Effectiveness of Physiotherapy Treatments. CMBES Proceedings. 2019;42. Lahiri BB, Bagavathiappan S, Jayakumar T, Philip J. Medical applications of infrared thermography: a review. Infrared physics & technology. 2012;55(4):221-35. Saarto T, Wiffen PJ. Antidepressants for neuropathic pain. Cochrane Database of Systematic Reviews. 2007(4). Shiwa M, Kishi T. Ndt-based assessment of damage: An overview. 2005. Ramesh V. A review on application of deep learning in thermography. International Journal of Engineering and Management Research (IJEMR). 2017;7(3):489-93. Rana A, Chauhan K. Computer vision and machine learning for image recognition: A review of the convolutional neural network (CNN) model. Asian Journal of Multidimensional Research. 2021;10(10):1023-9. Kijowski R, Liu F, Caliva F, Pedoia V. Deep learning for lesion detection, progression, and prediction of musculoskeletal disease. Journal of magnetic resonance imaging. 2020;52(6):1607-19. LeCun Y, Bengio Y, Hinton G. Deep learning. nature. 2015;521(7553):436-44. Wang EJ, Li W, Hawkins D, Gernsheimer T, Norby-Slycord C, Patel SN, editors. HemaApp: noninvasive blood screening of hemoglobin using smartphone cameras. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing; 2016. Additional Declarations No competing interests reported. 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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-4306725","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":299306255,"identity":"b2128323-4716-47b8-bb93-25fb40b48c5a","order_by":0,"name":"MOHAMMED ABOO SALEH MOHAMMED ABOO SALEH","email":"data:image/png;base64,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","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"MOHAMMED","middleName":"ABOO SALEH MOHAMMED ABOO","lastName":"SALEH","suffix":""}],"badges":[],"createdAt":"2024-04-22 14:59:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4306725/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4306725/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56026330,"identity":"ce273321-c3ed-4243-8b37-d158c96d82fc","added_by":"auto","created_at":"2024-05-07 17:07:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":155538,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMusculoskeletal Analysis Procedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFlowchart outlining the complete musculoskeletal analysis process, from infrared image capture to healthcare professional interpretation.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4306725/v1/1c9d89a53e74ae921ef44ef2.png"},{"id":56025512,"identity":"fec3b45c-96f1-4a54-89f6-16b92275b3f5","added_by":"auto","created_at":"2024-05-07 16:59:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":40547,"visible":true,"origin":"","legend":"\u003cp\u003eThe figure illustrates the different phases involved in uploading data and an infrared image captured in the application.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4306725/v1/48e6e188d4661c4f05799f64.png"},{"id":56025510,"identity":"829f7bd0-d634-4fbe-88b4-17db61bbbad6","added_by":"auto","created_at":"2024-05-07 16:59:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":117367,"visible":true,"origin":"","legend":"\u003cp\u003eFig.3.1 shows infrared images of a participant Before the therapy. The top panels use a rainbow colour scale to represent relative temperature variations, with a colour bar indicating the specific temperature range. The bottom panels display the same images in grayscale\u003c/p\u003e","description":"","filename":"3.1.png","url":"https://assets-eu.researchsquare.com/files/rs-4306725/v1/722a4beba675b2bc2ae12d82.png"},{"id":56025513,"identity":"0a62ae71-fe25-4343-b600-a1d9f6a543c5","added_by":"auto","created_at":"2024-05-07 16:59:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":171230,"visible":true,"origin":"","legend":"\u003cp\u003eFig.3.2. shows infrared images of a participant After the therapy. The top panels use a rainbow colour scale to represent relative temperature variations, with a colour bar indicating the specific temperature range. The bottom panels display the same images in grayscale, where brighter areas represent warmer temperatures and darker areas represent cooler temperatures. \u003cstrong\u003eHistogram of Thermal Temperature Image\u003c/strong\u003e: This data visualization shows the frequency of different temperature values in the image.\u003c/p\u003e","description":"","filename":"3.2.png","url":"https://assets-eu.researchsquare.com/files/rs-4306725/v1/5ac87fc9263e78e257e46fc5.png"},{"id":56025514,"identity":"f14a39ac-62d6-4252-a90d-ffac5d717429","added_by":"auto","created_at":"2024-05-07 16:59:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":197952,"visible":true,"origin":"","legend":"\u003cp\u003eFig.4. A thermal palette is a way to represent temperature variations using colour. Infrared cameras (eg.FLIR) typically use various Thermal palettes where white often represents higher temperature and black colour represents lower temperature.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4306725/v1/597f81427e74e5001f94ddc6.png"},{"id":56026339,"identity":"55c22074-72ca-4253-9be7-23e15da796ca","added_by":"auto","created_at":"2024-05-07 17:07:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1135301,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4306725/v1/1e4bf205-5ba3-4a34-be77-7a41e9ab9e85.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancing Musculoskeletal Healthcare through Mobile Application-Integrated Infrared Thermography: A Promising Approach for Diagnosis and Monitoring","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eMusculoskeletal injuries encompass a broad spectrum of conditions affecting the muscles, bones, ligaments, tendons, and joints, often arising from trauma, repetitive strain, or underlying health issues. Musculoskeletal pain is frequently characterized in words that imply a specific cause or precise site of tissue injury (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The diagnosis and treatment of these injuries present significant challenges, typically relying on subjective evaluations by healthcare providers. Medical applications already implemented in microscope/imaging systems have been developed as a platform for diagnosis at POC (Point Of Care)(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), infrared thermography, and phone-based oxygen level estimation system(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Medical applications may also assist clinical.\u003c/p\u003e \u003cp\u003eprofessionals with time management, patient data access, communication, consultation, patient monitoring, and medical training (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). HemaApp(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) from the University of Washington, for example, uses a sophisticated machine-learning approach to detect hemoglobin concentration using the phone's camera by employing external light sources such as an incandescent light bulb or a set of LEDs. However, these extra light sources are not widely available, and their use is limited in newer smartphones with an IR filter built into the camera lens (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTraditional diagnostic methods, such as physical examinations and imaging studies like X-rays or MRIs, have limitations in providing real-time, quantitative data on the physiological changes occurring within the body. Recent advancements in medical imaging have sparked interest in the use of infrared thermography for assessing musculoskeletal injuries. The treatment methods discussed here can also be used for non-specific musculoskeletal problems in general (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). While pain may appear to be non-specific from a musculoskeletal aspect, it is critical to recognize and evaluate particular processes such as central sensitization(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInfrared thermography is a non-invasive imaging technique that detects thermal patterns on the body's surface by measuring infrared radiation emitted by the skin. Changes in skin temperature can signify underlying physiological alterations, including inflammation, muscle tension, or alterations in blood circulation. As such, thermal imaging holds promise as a valuable tool for objectively assessing musculoskeletal injuries.\u003c/p\u003e \u003cp\u003eThis study aims to explore the integration of infrared thermography technology with mobile applications to enhance the objective assessment of musculoskeletal injuries, particularly in the context of massage therapy. The average normal body temperature is 37\u0026deg;C and a few degrees Celsius difference to this range is considered as physiologically abnormal (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In most cases, a raised temperature in different regions of the body is an indication of inflammation, tension, lack of blood circulation, etc(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). By capturing thermal images of patients' backs before and after therapy sessions, we seek to identify abnormal temperature patterns indicative of underlying conditions. Medical Infrared thermography (MIT) is a promising solution to measure the body temperature across a large area or the full body surface (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) The development of a mobile application facilitates data management and analysis, offering a novel approach to musculoskeletal injury diagnosis and treatment.\u003c/p\u003e \u003cp\u003eIn this research, we have developed a mobile application for the diagnosis of musculoskeletal injuries, wherein an infrared camera is connected to a mobile device. This integration allows clinicians to capture thermal images of patients' bodies using the mobile application, facilitating non-invasive and efficient assessment of musculoskeletal health.\u003c/p\u003e \u003cp\u003eThrough the mobile application, clinicians can easily access and analyze thermal images captured by the infrared camera, enabling the detection of abnormal temperature patterns associated with musculoskeletal conditions. The connectivity between the infrared camera and the mobile device represents a significant advancement in the field of musculoskeletal medicine, offering clinicians a convenient and accessible tool for diagnosing and monitoring patients' conditions in real time. By leveraging infrared technology and mobile platforms, our research aims to improve the diagnosis and treatment of individuals with musculoskeletal issues, ultimately enhancing patient outcomes and quality of care.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Musculoskeletal:\u003c/h2\u003e \u003cp\u003eThe musculoskeletal system encompasses bones, muscles, joints, and related tissues like cartilage, ligaments, tendons, and bursae. It supports both stability and movement in the body. Common complaints include neck pain, limb pain, low back pain, joint pain, and generalized discomfort, often leading to primary care visits. Low back pain, being prevalent, serves as an example due to its extensive treatment evidence. Treatments discussed here are applicable to various musculoskeletal issues. While pain may seem non-specific, it's essential to address specific processes like central sensitization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Clinical Treatment:\u003c/h2\u003e \u003cp\u003eClinical therapy for musculoskeletal pain comprises pharmacological and non-pharmacological approaches. Pharmacological treatments, including the WHO analgesic ladder, are less effective for non-specific, chronic, or widespread pain. Tricyclic antidepressants like amitriptyline show efficacy in neuropathic pain associated with conditions like fibromyalgia and low back pain. Non-pharmacological interventions, such as physical exercise and cognitive patient education, are cornerstone treatments, ideally delivered together as part of a multidisciplinary rehabilitation program. Various physical activities, from walking to swimming, have shown to reduce pain and improve function in musculoskeletal disorders. Close monitoring of pain and adverse effects is essential when using analgesics for chronic musculoskeletal conditions. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Thermography:\u003c/h2\u003e \u003cp\u003eThermography, utilizing infrared cameras, gauges surface temperature distribution to assess underlying structural issues or behaviour. It comes in two forms: passive and active. Passive thermography identifies temperature changes by heating the surface, revealing anomalies that may indicate underlying concerns. Active thermography, on the other hand, directly measures item temperature to pinpoint damage. Its applications span various fields such as agriculture, industry, medicine, and veterinary practice (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Deep Learning:\u003c/h2\u003e \u003cp\u003eDeep learning, a subset of machine learning, involves learning data representations through layers in a Deep Neural Network (DNN). Each layer transforms input data into increasingly abstract representations, culminating in predictions (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis architecture finds applications in musculoskeletal imaging, such as tissue segmentation, image reconstruction, and illness identification (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Inspired by the brain's interconnected neurons, DNNs consist of input, hidden, and output layers. Convolutional Neural Networks (CNNs), prevalent in medical imaging, feature convolutional and pooling layers (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Activation functions ensure only relevant features pass between layers. Typically trained with supervised learning using paired images and labels, CNNs are evaluated for diagnostic performance post-training.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Conclusion:\u003c/h2\u003e \u003cp\u003eIn this literature review we discussed every related paper with the study. But when we concentrated on the specifically musculoskeletal pain, we didn\u0026rsquo;t find any valid context for reference. For example, as mentioned earlier HemaApp (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)from the University of Washington, uses a sophisticated machine learning approach to detect hemoglobin concentration using the phone's camera by employing external light sources. That\u0026rsquo;s where this research is valuable for finding the pains and provide a new solution to the world. This study goal is to enhance the diagnosis and treatment of individuals with musculoskeletal issues by combining infrared mobile technology with a mobile application.\u003c/p\u003e \u003cp\u003eAccording to the findings, the benefit of this technique is to track and provide a proper solution for the clients.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. FUTURE WORKS","content":"\u003cp\u003eFuture research in this field could focus on optimizing deep learning algorithms for more accurate detection of thermal anomalies, conducting longitudinal studies to assess long-term treatment outcomes, and developing standardized protocols for thermal imaging in musculoskeletal assessments. Integrating additional sensor data and expanding research to include larger patient populations would further enhance the utility and applicability of thermal imaging technology in clinical practice.\u003c/p\u003e \u003cp\u003eFuture work can also focus on enhancing the mobile application's functionality and user experience, integrating features for real-time data analysis, automated report generation, and secure cloud storage. Interactive elements like educational resources and exercise programs could also be incorporated to empower patient engagement in rehabilitation. Exploring telemedicine and remote monitoring opportunities would extend the application's reach beyond clinical settings, enabling healthcare providers to assess and monitor patients' musculoskeletal health remotely. Collaboration with software developers and human-computer interaction experts would ensure the application meets diverse needs, enhancing its usability and impact in musculoskeletal healthcare.\u003c/p\u003e"},{"header":"4. CONCLUTION","content":"\u003cp\u003ethe integration of mobile applications with infrared thermography presents a promising avenue for enhancing musculoskeletal healthcare. The developed mobile application not only enables real-time thermal imaging but also provides clinicians with convenient access to patient data, facilitating efficient monitoring and analysis. Future research could explore further enhancements to the mobile application interface, such as incorporating features for data visualization, patient management, and treatment tracking. Additionally, efforts to optimize the application for compatibility with different smartphone models and operating systems would broaden its accessibility and usability in clinical settings. By leveraging the capabilities of mobile technology, infrared thermography can be seamlessly integrated into routine clinical practice, empowering healthcare professionals with valuable tools for diagnosing and managing musculoskeletal conditions effectively.\u003c/p\u003e"},{"header":"5. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e\u003cstrong\u003e5.1. Hardware Setup\u003c/strong\u003e:\u003c/h2\u003e\n \u003cp\u003eThe first phase of the hardware setup involved procuring a FLIR One infrared camera, meticulously selected for its seamless integration with smartphones and its exceptional high-resolution thermal imaging capabilities. The FLIR One camera, renowned for its compact design and portability, proved to be an ideal choice for capturing detailed thermal images of participants\u0026apos; musculoskeletal regions. With its advanced infrared sensor technology and precise thermal sensitivity, the camera ensured accurate detection and visualization of temperature variations on the body\u0026apos;s surface. The compatibility of the FLIR One camera with smartphones facilitated convenient image capture and real-time analysis through dedicated mobile applications, enhancing the efficiency and accessibility of the thermal imaging process. The images were obtained in a room with a temperature of about 20\u0026ordm;C (68\u0026deg;F). The camera was positioned at a height of 150 cm and a distance of 2 m away from the patient to capture a full-body image. This carefully selected hardware played a pivotal role in enabling comprehensive data collection and analysis for the study\u0026apos;s objectives.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e5.2. Software Setup:\u003c/h2\u003e\n \u003cp\u003eThe software setup commenced with the utilization of MATLAB, a multifaceted programming environment renowned for its extensive capabilities in scientific computing and algorithm development. MATLAB served as the primary platform for crafting sophisticated algorithms tailored to image processing, analysis, and visualization tasks. Leveraging MATLAB\u0026apos;s comprehensive toolboxes and libraries, including the Image Processing Toolbox and Computer Vision Toolbox, facilitated the creation of robust algorithms optimized for thermal image analysis. These algorithms were meticulously designed to enhance the accuracy of temperature measurements, identify thermal anomalies, and extract valuable insights from the captured thermal images.\u003c/p\u003e\n \u003cp\u003eIn tandem with MATLAB, Android Studio, a powerful integrated development environment (IDE) specifically tailored for Android application development, was employed to create a custom mobile application. This application was purpose-built to seamlessly integrate with the FLIR One infrared camera and smartphones, enabling efficient thermal image capture, storage, and analysis directly on mobile devices. By harnessing the capabilities of Android Studio, developers were able to design an intuitive and user-friendly interface for the mobile application, empowering users to capture thermal images effortlessly and conduct real-time analysis of musculoskeletal regions. The synergy between MATLAB and Android Studio facilitated a seamless workflow, enabling researchers to leverage advanced image processing algorithms within a mobile environment for enhanced data collection and analysis capabilities.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e5.3. Data Collection:\u003c/h2\u003e\n \u003cp\u003eThe data collection phase commenced with the recruitment of volunteers falling within the specified age range of 25 to 40 years, ensuring a diverse representation of participants for the study. Rigorous screening procedures were implemented to select individuals without pre-existing musculoskeletal conditions or contraindications that could potentially confound the study results. The data collected can be uploaded and stored in the mobile application for future implementation and patient records as shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e5.4. Therapy Sessions:\u003c/h2\u003e\n \u003cp\u003eParticipants underwent targeted traditional massage therapy aimed at musculoskeletal regions with thermal anomalies. Following therapy, a 15-minute rest period was observed to stabilize physiological responses. Post-therapy thermal imaging immediately followed the rest period to assess changes in temperature distribution. This synchronized approach enabled real-time evaluation of treatment effectiveness, providing valuable insights into the impact of massage therapy on thermal abnormalities. By allowing time for physiological equilibrium and minimizing potential confounding factors, the rest period ensured accurate thermal imaging results. Patients with persistent musculoskeletal pain disorders, such as fibromyalgia and low back pain, have discovered that tricyclic antidepressants (TCA), particularly amitryptiline, are effective in treating neuropathic pain(\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e). Physical exercise and patient education with a cognitive approach are the pillars of non-pharmacological treatment, which are best delivered in tandem as part of a multi-professional rehabilitation program(\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e). The difference between the image analysis before and after massage therapy is aligned in the Table 1\u003c/p\u003e\n \u003cp\u003eThe therapy sessions were conducted in a controlled environment, with careful temperature regulation to optimize imaging conditions. Licensed massage therapists administered the massages, utilizing manual manipulation techniques to alleviate tension and improve circulation in targeted areas. Throughout the sessions, participant comfort and safety were prioritized, with therapists adjusting techniques as needed to accommodate individual preferences. Feedback from participants was encouraged to ensure treatment effectiveness and address any concerns. The integration of thermal imaging with massage therapy allowed for comprehensive assessment and monitoring of musculoskeletal conditions. This holistic approach provides a promising avenue for enhancing diagnosis and treatment outcomes in individuals with thermal anomalies indicative of musculoskeletal issues. The complete musculoskeletal analysis is represented in the flow diagram in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e5.5. Image Analysis\u003c/h2\u003e\n \u003cp\u003eCustom algorithms were meticulously developed in MATLAB to automate the analysis of thermal images obtained before and after therapy sessions. These algorithms were specifically designed to enhance the identification of thermal anomalies and quantify temperature changes in musculoskeletal regions. Sophisticated image processing techniques were employed to precisely locate and segment the areas of interest within the thermal images. The thermal palette is a way to represent temperature variations using color. Infrared cameras typically use various Thermal palettes where white often represents higher temperature and black color represents lower temperature Initially as shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e the images were pre-processed to enhance contrast and reduce noise, ensuring optimal clarity for subsequent analysis.\u003c/p\u003e\n \u003cp\u003eSubsequently, the musculoskeletal regions were delineated using advanced segmentation algorithms, which effectively separated the target areas from surrounding tissues or background. This segmentation process involved the application of thresholding, edge detection, and morphological operations to accurately define the boundaries of the regions displaying thermal anomalies.\u003c/p\u003e\n \u003cp\u003eOnce the regions of interest were identified, the algorithms extracted temperature data from the thermal images for quantitative analysis. Temperature values were measured within these regions both before and after therapy, allowing for direct comparison and assessment of temperature changes over time. Statistical methods, such as mean temperature calculation and temperature distribution analysis, were employed to quantify the extent of thermal alterations associated with the therapy. Additionally, heat maps and temperature profiles were generated to visualize the spatial distribution of temperature changes across the musculoskeletal regions. Overall, the developed algorithms provided robust tools for objective assessment and interpretation of thermal imaging data, facilitating comprehensive analysis of the therapeutic effects on musculoskeletal conditions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e5.6. Statistical Analysis\u003c/h2\u003e\n \u003cp\u003eUtilizing MATLAB, a histogram analysis was employed to assess the significance of temperature changes observed between the pre and post-therapy images as shown in Fig. \u003cspan class=\"InternalRef\"\u003e3.1\u003c/span\u003e and Fig. 3.2. Histograms were generated to visualize the distribution of temperature values within the identified regions of interest before and after therapy. Thermography assesses the structural damage or behavior of what is under the surface by measuring the distribution of surface temperature(\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e) By plotting the frequency of temperature values against temperature bins, the histograms provided an intuitive representation of the temperature distribution and any shifts in temperature patterns following therapy.\u003c/p\u003e\n \u003cp\u003eStatistical tests, such as paired t-tests or non-parametric Wilcoxon signed-rank tests, can be conducted using the temperature data extracted from the histograms to determine histogram the significance of temperature changes.\u003c/p\u003e\n \u003cp\u003eThese tests evaluated whether there were statistically significant differences in temperature distributions between the pre and post-therapy images. The results of the statistical tests were interpreted to assess the efficacy of massage therapy in modulating thermal anomalies.\u003c/p\u003e\n \u003cp\u003eAdditionally, effect size measures, such as Cohen\u0026apos;s d or eta-squared, may have been calculated based on the histogram data to quantify the magnitude of temperature changes observed between the pre and post-therapy conditions. These effect size measures provided insights into the practical significance of the observed temperature alterations, further informing the evaluation of therapy effectiveness.\u003c/p\u003e\n \u003cp\u003eOverall, the combination of histogram analysis and statistical tests in MATLAB offered a comprehensive approach to assessing the significance of temperature changes before and after therapy. This methodology provided quantitative evidence supporting the efficacy of massage therapy in mitigating thermal anomalies associated with musculoskeletal conditions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e\u003cstrong\u003e5.7. Ethical Considerations\u003c/strong\u003e\u003c/h2\u003e\n \u003cp\u003eEthical considerations were diligently managed throughout the research endeavor, commencing with Institutional Review Board (IRB) approval to uphold human research standards. Informed consent procedures were meticulously followed, ensuring participants were fully informed about the study\u0026apos;s objectives, procedures, and potential risks before their involvement. Robust privacy protocols were implemented to maintain confidentiality, with participant identities anonymized to protect their privacy. Continuous monitoring and oversight mechanisms were in place to promptly address any ethical concerns or unforeseen issues that arose during the study. Adherence to the Declaration of Helsinki and institutional guidelines was paramount in upholding participant welfare and research integrity. Additionally, stringent measures were enacted to mitigate any potential discomfort or distress experienced by participants, prioritizing their well-being throughout the research process.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003eTable.1. In this table summarizes the key observations and clinical significance of thermal imaging before and after therapy in individuals with and without musculoskeletal issues.\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAspects\u003c/p\u003e\n \u003cp\u003eAspects\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBefore Therapy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAfter Therapy\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHealthy participants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRelatively uniform temperature distributions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConsistent thermal profiles, minimal temperature alterations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParticipants with MSK issues\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLocalized temperature variations, indicative of inflammation, tension, or circulation issues\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReduction in localized temperature anomalies, indicating therapeutic efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaseline measurements providing insights into physiological status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImmediate physiological responses to therapeutic interventions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClinical Significants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderstanding baseline thermal profiles for healthy individuals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAssessing treatment effectiveness and guiding therapeutic decisions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUtilizations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonitoring patient progress and optimizing therapy protocols\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTracking treatment outcomes and guiding patient care strategies\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo my first supervisor, Jean Ngoie who gave me the idea to work on this great research subject that I have always been interested in, thank you for your ultimate support and feedback, thank you for the regular chats which made me feel comfortable doing this work, you were a mentor and a dear friend. My Special thanks to the physiotherapist Catherine Louise Murrison who gave me support in the massage area of this research I would also like to show my gratitude to the people whom I sought for statistical analysis, Chunhui Li who have helped me a lot in my statistical work where I was completely lost. I would not have gotten any results without your guidance and wisdom. Finally, I would offer my sincere thanks to Scott McGregor from the University of Dundee Library and Learning Centre, who has helped me develop and structure my dissertation, you have saved no effort in helping, you are the kindest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the research described, ethical considerations were paramount throughout the study\u0026apos;s duration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe following details can be added to address ethical approval:\u0026nbsp;\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eInstitutional Review Board (IRB) Approval: Before commencing the study, approval from the Institutional Review Board was obtained to ensure compliance with ethical standards for human research. The IRB review process involved a thorough evaluation of the study protocol, including participant recruitment procedures, informed consent processes, data handling, and potential risks to participants.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eInformed Consent Procedures: Rigorous informed consent procedures were implemented to ensure that participants were fully informed about the study\u0026apos;s objectives, procedures, and potential risks before their involvement. Participants were provided with detailed information about the study, including its purpose, procedures, potential benefits, and any foreseeable risks or discomforts. They were given adequate time to review the consent form and ask questions before voluntarily agreeing to participate.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eParticipant Privacy and Confidentiality: Robust privacy protocols were implemented to safeguard participant confidentiality and privacy. Participant identities were anonymized to protect their privacy, and all data collected during the study were stored securely and accessed only by authorized personnel. Any personal information collected was kept confidential and used solely for research purposes.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eContinuous Monitoring and Oversight: Continuous monitoring and oversight mechanisms were in place throughout the study to promptly address any ethical concerns or unforeseen issues that arose. Researchers remained vigilant in monitoring participant welfare and research integrity, promptly addressing any ethical issues or deviations from the approved protocol.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAdherence to Ethical Guidelines: Adherence to ethical guidelines, including the Declaration of Helsinki and institutional guidelines, was paramount in upholding participant welfare and research integrity. Researchers ensured that all aspects of the study were conducted in accordance with ethical principles, including respect for participant autonomy, beneficence, non-maleficence, and justice.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eBy adhering to these ethical considerations and obtaining proper approval from the Institutional Review Board, the researchers ensured that the study was conducted ethically and with utmost regard for participant welfare and research integrity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo access the datasets used in this study, interested parties can request access through the corresponding author. Due to privacy and ethical considerations, access to the datasets may be subject to approval by the Institutional Review Board (IRB) or other relevant regulatory bodies. Additionally, data sharing may require compliance with data protection regulations and the completion of data use agreements.\u003c/p\u003e\n\u003cp\u003eFor inquiries regarding access to the datasets, please contact the corresponding author at [email protected]. Requests will be reviewed and processed in accordance with the applicable regulations and ethical guidelines governing data sharing and protection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest regarding the publication of this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBergman S. Management of musculoskeletal pain. Best Practice \u0026amp; Research Clinical Rheumatology. 2007;21(1):153-66.\u003c/li\u003e\n \u003cli\u003eHuang W, Luo S, Yang D, Zhang S. 基于智能手机的近红外成像, 测量和光谱技术在护理点诊断中的应用. Journal of Zhejiang University-SCIENCE B. 2021;22:171-89.\u003c/li\u003e\n \u003cli\u003eBui N, Nguyen A, Nguyen P, Truong H, Ashok A, Dinh T, et al., editors. Pho2: Smartphone based blood oxygen level measurement systems using near-ir and red wave-guided light. Proceedings of the 15th ACM conference on embedded network sensor systems; 2017.\u003c/li\u003e\n \u003cli\u003eGreen MS, Mathew JJ, Gundigi Venkatesh A, Green P, Tariq R. Utilization of smartphone applications by anesthesia providers. Anesthesiology Research and Practice. 2018;2018.\u003c/li\u003e\n \u003cli\u003eMain C. de C, Williams AC. Musculoskeletal pain. Br Med J. 2002;325:534-7.\u003c/li\u003e\n \u003cli\u003eWoolf CJ. Pain: moving from symptom control toward mechanism-specific pharmacologic management. Annals of internal medicine. 2004;140(6):441-51.\u003c/li\u003e\n \u003cli\u003eFaerber J, Ngoie J. Integration of a Mobile Application using Medical Infrared Imaging to improve the Effectiveness of Physiotherapy Treatments. CMBES Proceedings. 2019;42.\u003c/li\u003e\n \u003cli\u003eLahiri BB, Bagavathiappan S, Jayakumar T, Philip J. Medical applications of infrared thermography: a review. Infrared physics \u0026amp; technology. 2012;55(4):221-35.\u003c/li\u003e\n \u003cli\u003eSaarto T, Wiffen PJ. Antidepressants for neuropathic pain. Cochrane Database of Systematic Reviews. 2007(4).\u003c/li\u003e\n \u003cli\u003eShiwa M, Kishi T. Ndt-based assessment of damage: An overview. 2005.\u003c/li\u003e\n \u003cli\u003eRamesh V. A review on application of deep learning in thermography. International Journal of Engineering and Management Research (IJEMR). 2017;7(3):489-93.\u003c/li\u003e\n \u003cli\u003eRana A, Chauhan K. Computer vision and machine learning for image recognition: A review of the convolutional neural network (CNN) model. Asian Journal of Multidimensional Research. 2021;10(10):1023-9.\u003c/li\u003e\n \u003cli\u003eKijowski R, Liu F, Caliva F, Pedoia V. Deep learning for lesion detection, progression, and prediction of musculoskeletal disease. Journal of magnetic resonance imaging. 2020;52(6):1607-19.\u003c/li\u003e\n \u003cli\u003eLeCun Y, Bengio Y, Hinton G. Deep learning. nature. 2015;521(7553):436-44.\u003c/li\u003e\n \u003cli\u003eWang EJ, Li W, Hawkins D, Gernsheimer T, Norby-Slycord C, Patel SN, editors. HemaApp: noninvasive blood screening of hemoglobin using smartphone cameras. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing; 2016.\u003c/li\u003e\n\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":"","lastPublishedDoi":"10.21203/rs.3.rs-4306725/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4306725/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMusculoskeletal injuries, encompassing conditions affecting muscles, bones, ligaments, tendons, and joints, pose significant diagnostic and treatment challenges, often requiring subjective assessments by healthcare providers. In this study, an innovative approach was employed using infrared imaging technology integrated with a mobile application to enhance objective assessment, particularly during massage therapy. Thermal imaging captures of patients' backs during pre- and post-therapy sessions were conducted via the developed app to identify abnormal temperature patterns indicative of underlying conditions. This novel method of diagnosis offers a departure from traditional subjective assessments by providing quantitative thermal data for analysis. The utilization of deep learning algorithms and thermal image processing techniques further facilitated data management and analysis, offering a more precise diagnostic approach. The application's diagnostic capabilities, coupled with its ability to streamline data management, distinguish it from conventional diagnostic methods, offering potential advancements in musculoskeletal injury diagnosis and treatment. This paper elucidates the methods employed, highlights the observed results, underscores the potential of integrating advanced technologies for musculoskeletal injury diagnosis and treatment, and provides a brief overview of musculoskeletal injuries to contextualize the study.\u003c/p\u003e","manuscriptTitle":"Enhancing Musculoskeletal Healthcare through Mobile Application-Integrated Infrared Thermography: A Promising Approach for Diagnosis and Monitoring","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-07 16:59:15","doi":"10.21203/rs.3.rs-4306725/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":"8f2b8873-5704-48a6-b83f-50685bb622c8","owner":[],"postedDate":"May 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-07T16:59:15+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-07 16:59:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4306725","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4306725","identity":"rs-4306725","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}