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Cited by 444

The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.

IoT-Based Smart Rehabilitation System

Yuan Jie Fan, Yue Hong Yin, Li Da Xu, Yan Zeng, Fan Wu·2014·IEEE Transactions on Industrial Informatics

Abstract

Internet of Things (IoT) makes all objects become interconnected and smart, which has been recognized as the next technological revolution. As its typical case, IoT-based smart rehabilitation systems are becoming a better way to mitigate problems associated with aging populations and shortage of health professionals. Although it has come into reality, critical problems still exist in automating design and reconfiguration of such a system enabling it to respond to the patient’s requirements rapidly. This paper presents an ontology-based automating design methodology (ADM) for smart rehabilitation systems in IoT. Ontology aids computers in further understanding the symptoms and medical resources, which helps to create a rehabilitation strategy and reconfigure medical resources according to patients’ specific requirements quickly and automatically. Meanwhile, IoT provides an effective platform to interconnect all the resources and provides immediate information interaction. Preliminary experiments and clinical trials demonstrate valuable information on the feasibility, rapidity, and effectiveness of the proposed methodology.

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Metadata

Authors
Fan, Y. J., Yin, Y. H., Xu, L. D., Zeng, Y., & Wu, F.
Venue
IEEE Transactions on Industrial Informatics
Published
2014-05
Volume
10
Issue
2
Pages
1568-1577
ISSN
  • 1551-3203 (ISSN-L)
  • 1941-0050
OpenAlex
W1977777780
Cited by
444
References
38
FWCI
54.81
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: IoT-Based Smart Rehabilitation System. https://app.cochat.ai/papers/10.1109/tii.2014.2302583
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Fan, Y. J., Yin, Y. H., Xu, L. D., Zeng, Y., & Wu, F. (2014). IoT-Based Smart Rehabilitation System. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/tii.2014.2302583

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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