Technology-Based Compensation Assessment and Detection of Upper Extremity Activities of Stroke Survivors: Systematic Review

Journals

1. Judy L, Morrow C, Seo N. Development and evaluation of an efficient training program to facilitate the adoption of a novel neurorehabilitation device. Journal of Rehabilitation and Assistive Technologies Engineering 2023;10 View
2. Zhao Z, Wang J, Wang S, Wang R, Lu Y, Yuan Y, Chen J, Dai Y, Liu Y, Wang X, Pan Y, Gao S. Multimodal Sensing in Stroke Motor Rehabilitation. Advanced Sensor Research 2023;2(9) View
3. Bandini V, Carpinella I, Marzegan A, Jonsdottir J, Frigo C, Avanzino L, Pelosin E, Ferrarin M, Lencioni T. Surface-Electromyography-Based Co-Contraction Index for Monitoring Upper Limb Improvements in Post-Stroke Rehabilitation: A Pilot Randomized Controlled Trial Secondary Analysis. Sensors 2023;23(17):7320 View
4. Cheng H, Chin L, Kanzler C, Lehner R, Kuah C, Kager S, Josse E, Samkharadze T, Sidarta A, Gonzalez P, Lie E, Zbytniewska-Mégret M, Wee S, Liang P, Gassert R, Chua K, Lambercy O, Wenderoth N. Upper limb sensorimotor recovery in Asian stroke survivors: a study protocol for the development and implementation of a Technology-Assisted dIgitaL biOmaRker (TAILOR) platform. Frontiers in Neurology 2023;14 View
5. Ito D, Fukuda M, Hosoi Y, Hirose R, Teramae T, Kamimoto T, Yamada Y, Tsuji T, Noda T, Kawakami M. Optimizing shoulder elevation assist rate in exoskeletal rehabilitation based on muscular activity indices: a clinical feasibility study. BMC Neurology 2024;24(1) View
6. Fu Y, Zhang Y, Ye B, Babineau J, Zhao Y, Gao Z, Mihailidis A. Smartphone-Based Hand Function Assessment: Systematic Review. Journal of Medical Internet Research 2024;26:e51564 View
7. Ding K, Wang J, Wang X, Zhou L, Xiong D, Guo L. System for Detection and Quantitative Evaluation of Compensatory Movement in Post-Stroke Patients Based on Wearable Sensor and Machine Learning Algorithm. IEEE Sensors Journal 2024;24(14):22830 View
8. Xuan Chua M, Okubo Y, Peng S, Nho Do T, Hui Wang C, Wu L. Analysis of Fatigue-Induced Compensatory Movements in Bicep Curls: Gaining Insights for the Deployment of Wearable Sensors. IEEE Transactions on Medical Robotics and Bionics 2024;6(3):1147 View
9. Cocco E, Pournajaf S, Romano P, Morone G, Thouant C, Buscarini L, Manzia C, Cioeta M, Felzani G, Infarinato F, Franceschini M, Goffredo M. Comparative analysis of upper body kinematics in stroke, Parkinson's disease, and healthy subjects: An observational study using IMU-based targeted box and block test. Gait & Posture 2024;114:69 View
10. Sauerzopf L, Panduro C, Luft A, Kühnis B, Gavagnin E, Unger T, Awai C, Schönhammer J, Degenfellner J, Spiess M. Evaluating inter- and intra-rater reliability in assessing upper limb compensatory movements post-stroke: creating a ground truth through video analysis?. Journal of NeuroEngineering and Rehabilitation 2024;21(1) View
11. Herrera V, Albusac J, Angulo E, González-Morcillo C, de Los Reyes-Guzman A, Vallejo D. Virtual Reality-Assisted Goalkeeper Training for Upper Limb Rehabilitation in a Safe and Adapted Patient Environment. IEEE Access 2024;12:194256 View
12. Ito D, Kawakami M, Hosoi Y, Kamimoto T, Yamada Y, Takemura R, Tsuji T. Development of a quantitative assessment for abnormal flexor synergy index in patients with stroke: a validity and responsiveness study. Journal of NeuroEngineering and Rehabilitation 2024;21(1) View
13. Herrera V, Albusac J, Castro-Schez J, González-Morcillo C, Monekosso D, Pacheco S, Perales R, de los Reyes-Guzmán A. Creating adapted environments: enhancing accessibility in virtual reality for upper limb rehabilitation through automated element adjustment. Virtual Reality 2025;29(1) View
14. Afzal H, Louhichi B, Alrasheedi N. Challenges in Combining EMG, Joint Moments, and GRF from Marker-Less Video-Based Motion Capture Systems. Bioengineering 2025;12(5):461 View
15. Murat G, Doğan M, Onursal Kılınç Ö, Aksu Yıldırım S, Kılınç M. Upper extremity function assessment test using virtual environment technique in stroke survivors. Somatosensory & Motor Research 2025:1 View
16. Rocha C, Carneiro I, Torres M, Oliveira H, Solteiro Pires E, Silva M. Post-stroke upper limb rehabilitation: clinical practices, compensatory movements, assessment, and trends. Progress in Biomedical Engineering 2025;7(4):042001 View
17. Chua M, Peng S, Nho Do T, Hui Wang C, Wu L. A Wearable Strain-Sensor-Based Shoulder Patch for Fatigue Detection in Bicep Curls. IEEE Transactions on Instrumentation and Measurement 2025;74:1 View
18. Jung J, Kim J. Sensor-Based Analysis of Upper Limb Motor Coordination After Stroke: Insights from EMG, ROM, and Motion Data During the Wolf Motor Function Test. Applied Sciences 2025;15(17):9836 View
19. Somville M, Rosselli Z, Ducoffre E, Penta M, Smeesters T, Bleyenheuft Y, Saussez G. Use of Accelerometers to Monitor Motor Activity During HABIT-ILE for Chronic Stroke: An Exploratory Study. Sensors 2025;25(21):6656 View
20. Unger T, Kühnis B, Sauerzopf L, Spiess M, de Spindler A, Luft A, Easthope Awai C, Schönhammer J, Gavagnin E. Using deep learning to detect upper limb compensation in individuals post-stroke using consumer-grade webcams—A feasibility study. Frontiers in Medicine 2025;12 View
21. Wagh V, Scott M, Andrushko J, Jones C, Larssen B, Boyd L, Kraeutner S. Using MediaPipe to track upper-limb reaching movements after stroke: a proof-of-principle study. Journal of NeuroEngineering and Rehabilitation 2025 View

Books/Policy Documents

1. Tello E, Yanadel A, Perez M, López N. Advances in Bioengineering and Clinical Engineering. View
2. Srinivasan S, Rajavel N. AI Technologies and Advancements for Psychological Well-Being and Healthcare. View