This paper is in the following e-collection/theme issue:

Games for Rehabilitation (94) Extended Reality, Virtual Reality and Virtual Worlds (560) mHealth for Rehabilitation (263) Emerging Technologies for Rehabilitation (261) Stroke (163) Cognitive and Neurorehabilitation (262)

Published on in Vol 9, No 1 (2021): Jan-Mar

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/20916, first published .
A Depth Camera–Based, Task-Specific Virtual Reality Rehabilitation Game for Patients With Stroke: Pilot Usability Study

A Depth Camera–Based, Task-Specific Virtual Reality Rehabilitation Game for Patients With Stroke: Pilot Usability Study

A Depth Camera–Based, Task-Specific Virtual Reality Rehabilitation Game for Patients With Stroke: Pilot Usability Study

Authors of this article:

Yangfan Xu1, 2 Author Orcid Image ;   Meiqinzi Tong1 Author Orcid Image ;   Wai-Kit Ming3 Author Orcid Image ;   Yangyang Lin2 Author Orcid Image ;   Wangxiang Mai1 Author Orcid Image ;   Weixin Huang4 Author Orcid Image ;   Zhuoming Chen1 Author Orcid Image
Article Authors Cited by (19) Tweetations (10) Metrics

Journals

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  4. 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
  5. Afsar M, Saqib S, Aladfaj M, Alatiyyah M, Alnowaiser K, Aljuaid H, Jalal A, Park J. Body-Worn Sensors for Recognizing Physical Sports Activities in Exergaming via Deep Learning Model. IEEE Access 2023;11:12460 View
  6. Nair B, Sakthivel N. A Deep Learning-Based Upper Limb Rehabilitation Exercise Status Identification System. Arabian Journal for Science and Engineering 2023;48(2):1237 View
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  8. Martins T, Carvalho V, Soares F, Leão C. Physioland: a motivational complement of physical therapy for patients with neurological diseases. Multimedia Tools and Applications 2024;83(4):12035 View
  9. Lim D, Pei W, Lee J, Musselman K, Masani K. Feasibility of using a depth camera or pressure mat for visual feedback balance training with functional electrical stimulation. BioMedical Engineering OnLine 2024;23(1) View
  10. Sun W, Hsu Y, Liu Y, Tien C, Su Y, Tsai S, Sun C. Optical lens optimization design and tolerance analysis for a depth camera. Optik 2024;302:171711 View
  11. Pan H, Wang H, Li D, Zhu K, Gao Y, Yin R, Shull P. Automated, IMU-based spine angle estimation and IMU location identification for telerehabilitation. Journal of NeuroEngineering and Rehabilitation 2024;21(1) View
  12. Baklouti S, Chaker A, Rezgui T, Sahbani A, Bennour S, Laribi M. A Novel IMU-Based System for Work-Related Musculoskeletal Disorders Risk Assessment. Sensors 2024;24(11):3419 View
  13. Zhang C, Yu S. The Technology to Enhance Patient Motivation in Virtual Reality Rehabilitation: A Review. Games for Health Journal 2024;13(4):215 View
  14. Xie W, Xu C, Gong Y, Wang Y, Liu Y, Zhang J, Zhang Q, Zheng Z, Yang S. DeepBreath: Breathing Exercise Assessment with a Depth Camera. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2024;8(3):1 View

Books/Policy Documents

  1. Rito D, Pinheiro C, Figueiredo J, Santos C. Robotics in Natural Settings. View