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Cited by 382

The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.

Towards customizable games for stroke rehabilitation

Alankus, Gazihan, Lazar, Amanda, May, Matt, Kelleher, Caitlin·2010·Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Abstract

Stroke is the leading cause of long term disability among adults in industrialized nations. The partial paralysis that stroke patients often experience can make independent living difficult or impossible. Research suggests that many of these patients could recover by performing hundreds of daily repetitions of motions with their affected limbs. Yet, only 31% of patients perform the exercises recommended by their therapists. Home-based stroke rehabilitation games may help motivate stroke patients to perform the necessary exercises to recover. In this paper, we describe a formative study in which we designed and user tested stroke rehabilitation games with both stroke patients and therapists. We describe the lessons we learned about what makes games useful from a therapeutic point of view.

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Metadata

Authors
Alankus, G., Lazar, A., May, M., & Kelleher, C.
Venue
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Publisher
ACM
Published
2010-04-10
Pages
2113-2122
OpenAlex
W2163580112
Cited by
382
References
25
FWCI
48.79
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Towards customizable games for stroke rehabilitation. https://app.cochat.ai/papers/10.1145/1753326.1753649
The paper
Alankus, G., Lazar, A., May, M., & Kelleher, C. (2010). Towards customizable games for stroke rehabilitation. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/1753326.1753649

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.