The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Virtual reality-enhanced stroke rehabilitation
Abstract
A personal computer (PC)-based desktop virtual reality (VR) system was developed for rehabilitating hand function in stroke patients. The system uses two input devices, a CyberGlove and a Rutgers Master II-ND (RMII) force feedback glove, allowing user interaction with a virtual environment. This consists of four rehabilitation routines, each designed to exercise one specific parameter of hand movement: range, speed, fractionation or strength. The use of performance-based target levels is designed to increase patient motivation and individualize exercise difficulty to a patient's current state. Pilot clinical trials have been performed using the above system combined with noncomputer tasks, such as pegboard insertion or tracing of two-dimensional (2-D) patterns. Three chronic stroke patients used this rehabilitation protocol daily for two weeks. Objective measurements showed that each patient showed improvement on most of the hand parameters over the course of the training. Subjective evaluation by the patients was also positive. This technical report focuses on this newly developed technology for VR rehabilitation.
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Cites
Nudo, R. J., Wise, B. M., SiFuentes, F., & Milliken, G. W. (1996) · Science · doi:10.1126/science.272.5269.1791 · Cited by 1,911
Augmented Feedback Presented in a Virtual Environment Accelerates Learning of a Difficult Motor Task
Todorov, E., Shadmehr, R., & Bizzi, E. (1997) · Journal of Motor Behavior · doi:10.1080/00222899709600829 · Cited by 314
Cited by
Virtual Environments for Motor Rehabilitation: Review
Holden, M. K. (2005) · CyberPsychology & Behavior · doi:10.1089/cpb.2005.8.187 · Cited by 1,154
A SWOT Analysis of the Field of Virtual Reality Rehabilitation and Therapy
Rizzo, A. “., & Kim, G. J. (2005) · Presence: Teleoperators and Virtual Environments · doi:10.1162/1054746053967094 · Cited by 877
Human motion tracking for rehabilitation-A survey
Zhou, H., & Hu, H. (2008) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2007.09.001 · Cited by 813
Robot-based hand motor therapy after stroke
Takahashi, C. D., Der-Yeghiaian, L., Le, V., Motiwala, R. R., & Cramer, S. C. (2008) · Brain · doi:10.1093/brain/awm311 · Cited by 624
Towards customizable games for stroke rehabilitation
Alankus, G., Lazar, A., May, M., & Kelleher, C. (2010) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1753326.1753649 · Cited by 382
Metadata
- Authors
- Jack, D., Boian, R., Merians, A. S., Tremaine, M., Burdea, G. C., Adamovich, S. V., Recce, M., & Poizner, H.
- Venue
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Published
- 2001-09
- Volume
- 9
- Issue
- 3
- Pages
- 308-318
- ISSN
- 1534-4320 (ISSN-L)
- 1558-0210
- OpenAlex
- W2098089040
- Cited by
- 733
- References
- 2
- FWCI
- 11.06
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Jack, D., Boian, R., Merians, A. S., Tremaine, M., Burdea, G. C., Adamovich, S. V., Recce, M., & Poizner, H. (2001). Virtual reality-enhanced stroke rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering. https://doi.org/10.1109/7333.948460
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
