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Soft Physiology Sensors and Machine Learning to Enhance Spinal Cord Injury and Stroke Rehabilitation Outcomes in Home Settings

[+] Author Affiliations
Tzu-Hao Huang, Jianfu Yang, Eljona Pushaj, Viktor Silvanov, Shuangyue Yu, Xiaolong Yang, Hao Su

City University of New York, City College, New York, NY

Shuo-Hsiu Chang, Gerard Francisco

University of Texas Health Science Center at Houston, Houston, TX

Paper No. DMD2019-3267, pp. V001T05A001; 3 pages
doi:10.1115/DMD2019-3267
From:
  • 2019 Design of Medical Devices Conference
  • 2019 Design of Medical Devices Conference
  • Minneapolis, Minnesota, USA, April 15–18, 2019
  • ISBN: 978-0-7918-4103-7
  • Copyright © 2019 by ASME

abstract

This paper presents the design and fabrication of a textile-based soft Electromyography (EMG) sensor and machine-learning-based methods to detect muscle spasticity. The textile EMG sensor is flexible, foldable, stretchable, washable for multiple times, and easily customizable to meet the heterogeneous needs of SCI individuals. The machine learning algorithms that can estimate the muscle status and the performance of functional ADLs by classification of function ADLs and the detection of muscle spasticity. The soft textronic sensors, its intelligent machine learning algorithms, and biofeedback-based rehabilitation has the potential to enable home-based rehabilitation and encourage more manipulation for function ADLs and independence in SCI and stroke individuals.

Copyright © 2019 by ASME

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