Published on in Vol 8 , No 7 (2020) :July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/17216, first published .
Development and Clinical Evaluation of a Web-Based Upper Limb Home Rehabilitation System Using a Smartwatch and Machine Learning Model for Chronic Stroke Survivors: Prospective Comparative Study

Development and Clinical Evaluation of a Web-Based Upper Limb Home Rehabilitation System Using a Smartwatch and Machine Learning Model for Chronic Stroke Survivors: Prospective Comparative Study

Development and Clinical Evaluation of a Web-Based Upper Limb Home Rehabilitation System Using a Smartwatch and Machine Learning Model for Chronic Stroke Survivors: Prospective Comparative Study

Journals

  1. Watanabe O, Narita N, Katsuki M, Ishida N, Cai S, Otomo H, Yokota K. Prediction Model of Deep Learning for Ambulance Transports in Kesennuma City by Meteorological Data. Open Access Emergency Medicine 2021;Volume 13:23 View
  2. Burns S, Terblanche M, Perea J, Lillard H, DeLaPena C, Grinage N, MacKinen A, Cox E. mHealth Intervention Applications for Adults Living With the Effects of Stroke: A Scoping Review. Archives of Rehabilitation Research and Clinical Translation 2021;3(1):100095 View
  3. Calabrò R. Teleneurorehabilitation in the COVID-19 Era: What Are We Doing Now and What Will We Do Next?. Medical Sciences 2021;9(1):15 View
  4. Joo S, Lee C, Joo N, Kim C. Feasibility and Effectiveness of a Motion Tracking-Based Online Fitness Program for Office Workers. Healthcare 2021;9(5):584 View
  5. Avila F, McLeod C, Huayllani M, Boczar D, Giardi D, Bruce C, Carter R, Forte A. Wearable electronic devices for chronic pain intensity assessment: A systematic review. Pain Practice 2021;21(8):955 View
  6. Khoshrounejad F, Hamednia M, Mehrjerd A, Pichaghsaz S, Jamalirad H, Sargolzaei M, Hoseini B, Aalaei S. Telehealth-Based Services During the COVID-19 Pandemic: A Systematic Review of Features and Challenges. Frontiers in Public Health 2021;9 View
  7. Kim G, Parnandi A, Eva S, Schambra H. The use of wearable sensors to assess and treat the upper extremity after stroke: a scoping review. Disability and Rehabilitation 2022;44(20):6119 View
  8. Xie Y, Lu L, Gao F, He S, Zhao H, Fang Y, Yang J, An Y, Ye Z, Dong Z. Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare. Current Medical Science 2021;41(6):1123 View
  9. Bonnechère B. Integrating Rehabilomics into the Multi-Omics Approach in the Management of Multiple Sclerosis: The Way for Precision Medicine?. Genes 2022;14(1):63 View
  10. Aphiphaksakul P, Siriphorn A, Pinzon R. Home-based exercise using balance disc and smartphone inclinometer application improves balance and activity of daily living in individuals with stroke: A randomized controlled trial. PLOS ONE 2022;17(11):e0277870 View
  11. Dias L, Vianna H, Barbosa J. Human behaviour data analysis and noncommunicable diseases: a systematic mapping study. Behaviour & Information Technology 2022:1 View
  12. Uswatte G, Taub E, Lum P, Brennan D, Barman J, Bowman M, Taylor A, McKay S, Sloman S, Morris D, Mark V. Tele-rehabilitation of upper-extremity hemiparesis after stroke: Proof-of-concept randomized controlled trial of in-home Constraint-Induced Movement therapy. Restorative Neurology and Neuroscience 2021;39(4):303 View
  13. Han D, Ding E, Cho C, Jung H, Dickson E, Mohagheghian F, Peitzsch A, DiMezza D, Tran K, McManus D, Chon K. A Smartwatch System for Continuous Monitoring of Atrial Fibrillation in Older Adults After Stroke or Transient Ischemic Attack: Application Design Study. JMIR Cardio 2023;7:e41691 View
  14. Hu J, Zou J, Wan Y, Yao Q, Dong P, Li G, Wu X, Zhang L, Liang D, Zeng Q, Huang G. Rehabilitation of motor function after stroke: A bibliometric analysis of global research from 2004 to 2022. Frontiers in Aging Neuroscience 2022;14 View
  15. Jin P, Jiang W, Bao Q, Wei W, Jiang W. Predictive nomogram for soft robotic hand rehabilitation of patients with intracerebral hemorrhage. BMC Neurology 2022;22(1) View
  16. Lee W, Schwartz N, Bansal A, Khor S, Hammarlund N, Basu A, Devine B. A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 1—Data From Wearable Devices. Value in Health 2023;26(2):292 View
  17. Li Q, Liu Y, Zhu J, Chen Z, Liu L, Yang S, Zhu G, Zhu B, Li J, Jin R, Tao J, Chen L. Upper-Limb Motion Recognition Based on Hybrid Feature Selection: Algorithm Development and Validation. JMIR mHealth and uHealth 2021;9(9):e24402 View
  18. Boukhennoufa I, Zhai X, Utti V, Jackson J, McDonald-Maier K. Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review. Biomedical Signal Processing and Control 2022;71:103197 View
  19. Bin K, De Pretto L, Sanchez F, Battistella L. Digital Platform to Continuously Monitor Patients Using a Smartwatch: Preliminary Report. JMIR Formative Research 2022;6(9):e40468 View
  20. Boyer P, Burns D, Whyne C. Evaluation of at-home physiotherapy. Bone & Joint Research 2023;12(3):165 View
  21. Kocak U, Guran O, Kalkan S, Kaya E, Kurt M, Karatosun V, Unver B. Assessing the knee flexion range of motion after total knee arthroplasty: Technology versus senses. Journal of Bodywork and Movement Therapies 2021;28:547 View
  22. Khoja A, Ali N, Akber N, Harrison J, Kazim F. Role of virtual modality for stroke caregivers in facilitating stroke survivors and assessing their perceptions in the midst of COVID-19 pandemic. F1000Research 2022;11:1142 View
  23. Fulk G. Artificial Intelligence and Neurologic Physical Therapy. Journal of Neurologic Physical Therapy 2023;47(1):1 View
  24. Fan Y, Ma Y, Zhang Y, Sun C, Che H. A Retrospective Analysis of Internet-Based Sharing Nursing Service Appointment Data. Computational and Mathematical Methods in Medicine 2022;2022:1 View
  25. 孙 天. Feasibility of Family Rehabilitation Model for Cerebral Stroke Patients. Advances in Clinical Medicine 2022;12(04):2493 View
  26. Liu F, Bao G, Yan M, Lin G. A decision support system for primary headache developed through machine learning. PeerJ 2022;10:e12743 View
  27. Ding K, Zhang B, Ling Z, Chen J, Guo L, Xiong D, Wang J. Quantitative Evaluation System of Wrist Motor Function for Stroke Patients Based on Force Feedback. Sensors 2022;22(9):3368 View
  28. Nikolaev V, Nikolaev A. Recent trends in telerehabilitation of stroke patients: A narrative review. NeuroRehabilitation 2022;51(1):1 View
  29. Yang R, Zhang Y, Xu M, Ma J, Teekaraman Y. Image Features of Magnetic Resonance Angiography under Deep Learning in Exploring the Effect of Comprehensive Rehabilitation Nursing on the Neurological Function Recovery of Patients with Acute Stroke. Contrast Media & Molecular Imaging 2021;2021:1 View
  30. Guo L, Wang J, Wu Q, Li X, Zhang B, Zhou L, Xiong D. Clinical Study of a Wearable Remote Rehabilitation Training System for Patients With Stroke: Randomized Controlled Pilot Trial. JMIR mHealth and uHealth 2023;11:e40416 View
  31. Mennella C, Maniscalco U, De Pietro G, Esposito M. The Role of Artificial Intelligence in Future Rehabilitation Services: A Systematic Literature Review. IEEE Access 2023;11:11024 View
  32. Bonura A, Motolese F, Capone F, Iaccarino G, Alessiani M, Ferrante M, Calandrelli R, Lazzaro V, Pilato F. Smartphone App in Stroke Management: A Narrative Updated Review. Journal of Stroke 2022;24(3):323 View
  33. Lee K, Choi M, Jeoung B. Effectiveness of Rehabilitation Exercise in Improving Physical Function of Stroke Patients: A Systematic Review. International Journal of Environmental Research and Public Health 2022;19(19):12739 View
  34. Guo C, Chiesa P, de Moor C, Fazeli M, Schofield T, Hofer K, Belachew S, Scotland A. Digital Devices for Assessing Motor Functions in Mobility-Impaired and Healthy Populations: Systematic Literature Review. Journal of Medical Internet Research 2022;24(11):e37683 View
  35. Song Y, Zhang W, Li Q, Ma W, Khalaf O. Medical Data Acquisition and Internet of Things Technology-Based Cerebral Stroke Disease Prevention and Rehabilitation Nursing Mobile Medical Management System. Computational and Mathematical Methods in Medicine 2022;2022:1 View
  36. Di Spirito F. Integrating P4 Medicine in Teledentistry and M-Health in Oral, Dental, and Periodontal Care. Journal of Personalized Medicine 2023;13(1):111 View
  37. Huang J, Hartanti I, Colin M, Pitaloka D. Telemedicine and artificial intelligence to support self-isolation of COVID-19 patients: Recent updates and challenges. DIGITAL HEALTH 2022;8:205520762211006 View
  38. Lin C, Chien T, Chen Y, Lee Y, Su S. An app to classify a 5-year survival in patients with breast cancer using the convolutional neural networks (CNN) in Microsoft Excel. Medicine 2022;101(4):e28697 View
  39. Wang K, Gu L, Liu W, Xu C, Yin C, Liu H, Rong L, Li W, Wei X. The predictors of death within 1 year in acute ischemic stroke patients based on machine learning. Frontiers in Neurology 2023;14 View
  40. Toh S, Chia P, Fong K. Effectiveness of home-based upper limb rehabilitation in stroke survivors: A systematic review and meta-analysis. Frontiers in Neurology 2022;13 View
  41. Sun X, Ding J, Dong Y, Ma X, Wang R, Jin K, Zhang H, Zhang Y. A Survey of Technologies Facilitating Home and Community-Based Stroke Rehabilitation. International Journal of Human–Computer Interaction 2023;39(5):1016 View
  42. Toh S, Fong K, Gonzalez P, Tang Y. Application of Home-Based Wearable Technologies in Physical Rehabilitation for Stroke: A Scoping Review. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2023;31:1614 View
  43. Zhuparris A, Maleki G, Koopmans I, Doll R, Voet N, Kraaij W, Cohen A, van Brummelen E, De Maeyer J, Groeneveld G. Smartphone and Wearable Sensors for the Estimation of Facioscapulohumeral Muscular Dystrophy Disease Severity: Cross-sectional Study. JMIR Formative Research 2023;7:e41178 View
  44. Triantafyllidis A, Segkouli S, Zygouris S, Michailidou C, Avgerinakis K, Fappa E, Vassiliades S, Bougea A, Papagiannakis N, Katakis I, Mathioudis E, Sorici A, Bajenaru L, Tageo V, Camonita F, Magga-Nteve C, Vrochidis S, Pedullà L, Brichetto G, Tsakanikas P, Votis K, Tzovaras D. Mobile App Interventions for Parkinson’s Disease, Multiple Sclerosis and Stroke: A Systematic Literature Review. Sensors 2023;23(7):3396 View
  45. Yang T, Hu Y, Pan X, Lou S, Zou J, Deng Q, Zhang Q, Zhou J, Zhu J. Interpretable Machine Learning Model Predicting Early Neurological Deterioration in Ischemic Stroke Patients Treated with Mechanical Thrombectomy: A Retrospective Study. Brain Sciences 2023;13(4):557 View
  46. Bovens D, van Baarle E, Molewijk B. Personal health monitoring in the armed forces – scouting the ethical dimension. BMC Medical Ethics 2023;24(1) View
  47. Yoshida Y, Yuda E. Workout Detection by Wearable Device Data Using Machine Learning. Applied Sciences 2023;13(7):4280 View
  48. Rosiński J, Kotlarz P, Rojek I, Mikołajewski D. Machine Learning Classification for a Second Opinion System in the Selection of Assistive Technology in Post-Stroke Patients. Applied Sciences 2023;13(9):5444 View

Books/Policy Documents

  1. Klamroth-Marganska V, Giovanoli S, Easthope C, Schönhammer J. Neurorehabilitation Technology. View
  2. Uslu G, Unal B, Aydın A, Baydere S. Integrating Artificial Intelligence and IoT for Advanced Health Informatics. View
  3. Adans-Dester C, Lang C, Reinkensmeyer D, Bonato P. Neurorehabilitation Technology. View
  4. Ferreira R, Santos R, Sousa A. Exploring the Convergence of Computer and Medical Science Through Cloud Healthcare. View
  5. Wang X, Gao H, Ma T, Yu J. Intelligent Robotics and Applications. View
  6. Azam I, Usman M. Emerging Technologies During the Era of COVID-19 Pandemic. View
  7. Selvaganapathy S, Hema Priya N, Rathika P, Mohana Lakshmi M. Computer Vision and Robotics. View