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Cited by 357

The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.

Automating Arm Movement Training Following Severe Stroke: Functional Exercises With Quantitative Feedback in a Gravity-Reduced Environment

Open access
Sanchez, R.J., Liu, J., Rao, S., Shah, P., Smith, R., Rahman, T., Cramer, S.C., Bobrow, J.E., Reinkensmeyer, D.J.·2006·IEEE Transactions on Neural Systems and Rehabilitation Engineering
DOI 10.1109/tnsre.2006.881553
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Abstract

An important goal in rehabilitation engineering is to develop technology that allows individuals with severe motor impairment to practice arm movement without continuous supervision from a rehabilitation therapist. This paper describes the development of such a system, called Therapy WREX or ("T-WREX"). The system consists of an orthosis that assists in arm movement across a large workspace, a grip sensor that detects hand grip pressure, and software that simulates functional activities. The arm orthosis is an instrumented, adult-sized version of the Wilmington Robotic Exoskeleton (WREX), which is a five degrees-of-freedom mechanism that passively counterbalances the weight of the arm using elastic bands. After providing a detailed design description of T-WREX, this paper describes two pilot studies of the system's capabilities. The first study demonstrated that individuals with chronic stroke whose arm function is compromised in a normal gravity environment can perform reaching and drawing movements while using T-WREX. The second study demonstrated that exercising the affected arm of five people with chronic stroke with T-WREX over an eight week period improved unassisted movement ability (mean change in Fugl-Meyer score was 5 points +/- 2 SD; mean change in range of motion of reaching was 10%, p < 0.001). These results demonstrate the feasibility of automating upper-extremity rehabilitation therapy for people with severe stroke using passive gravity assistance, a grip sensor, and simple virtual reality software.

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Metadata

Authors
Sanchez, R. J., Liu, J., Rao, S., Shah, P., Smith, R., Rahman, T., Cramer, S. C., Bobrow, J. E., & Reinkensmeyer, D. J.
Venue
IEEE Transactions on Neural Systems and Rehabilitation Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2006-09
Volume
14
Issue
3
Pages
378-389
ISSN
  • 1534-4320 (ISSN-L)
  • 1558-0210
DOI
10.1109/tnsre.2006.881553
OpenAlex
W2158244378
Cited by
357
References
21
FWCI
22.21
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Automating Arm Movement Training Following Severe Stroke: Functional Exercises With Quantitative Feedback in a Gravity-Reduced Environment. https://app.cochat.ai/papers/10.1109/tnsre.2006.881553
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Sanchez, R. J., Liu, J., Rao, S., Shah, P., Smith, R., Rahman, T., Cramer, S. C., Bobrow, J. E., & Reinkensmeyer, D. J. (2006). Automating Arm Movement Training Following Severe Stroke: Functional Exercises With Quantitative Feedback in a Gravity-Reduced Environment. IEEE Transactions on Neural Systems and Rehabilitation Engineering. https://doi.org/10.1109/tnsre.2006.881553

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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.