Soft physiology sensors and machine learning to enhance spinal cord injury and stroke rehabilitation outcomes in home settings

Tzu Hao Huang; Jianfu Yang; Eljona Pushaj; Viktor Silvanov; Shuangyue Yu; Xiaolong Yang; Hao Su; Shuo Hsiu Chang; Gerard Francisco

doi:10.1115/DMD2019-3267

Soft physiology sensors and machine learning to enhance spinal cord injury and stroke rehabilitation outcomes in home settings

Tzu Hao Huang, Jianfu Yang, Eljona Pushaj, Viktor Silvanov, Shuangyue Yu, Xiaolong Yang, Hao Su, Shuo Hsiu Chang, Gerard Francisco

Biomedical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English (US)
Title of host publication	Frontiers in Biomedical Devices, BIOMED - 2019 Design of Medical Devices Conference, DMD 2019
Publisher	American Society of Mechanical Engineers (ASME)
ISBN (Electronic)	9780791841037
DOIs	https://doi.org/10.1115/DMD2019-3267
State	Published - 2019
Event	2019 Design of Medical Devices Conference, DMD 2019 - Minneapolis, United States Duration: Apr 15 2019 → Apr 18 2019

Publication series

Name	Frontiers in Biomedical Devices, BIOMED - 2019 Design of Medical Devices Conference, DMD 2019

Conference

Conference	2019 Design of Medical Devices Conference, DMD 2019
Country/Territory	United States
City	Minneapolis
Period	4/15/19 → 4/18/19

ASJC Scopus subject areas

Biomedical Engineering

Access to Document

10.1115/DMD2019-3267

Cite this

Huang, T. H., Yang, J., Pushaj, E., Silvanov, V., Yu, S., Yang, X., Su, H., Chang, S. H., & Francisco, G. (2019). Soft physiology sensors and machine learning to enhance spinal cord injury and stroke rehabilitation outcomes in home settings. In Frontiers in Biomedical Devices, BIOMED - 2019 Design of Medical Devices Conference, DMD 2019 (Frontiers in Biomedical Devices, BIOMED - 2019 Design of Medical Devices Conference, DMD 2019). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/DMD2019-3267

Soft physiology sensors and machine learning to enhance spinal cord injury and stroke rehabilitation outcomes in home settings. / Huang, Tzu Hao; Yang, Jianfu; Pushaj, Eljona et al.
Frontiers in Biomedical Devices, BIOMED - 2019 Design of Medical Devices Conference, DMD 2019. American Society of Mechanical Engineers (ASME), 2019. (Frontiers in Biomedical Devices, BIOMED - 2019 Design of Medical Devices Conference, DMD 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Huang, TH, Yang, J, Pushaj, E, Silvanov, V, Yu, S, Yang, X, Su, H, Chang, SH & Francisco, G 2019, Soft physiology sensors and machine learning to enhance spinal cord injury and stroke rehabilitation outcomes in home settings. in Frontiers in Biomedical Devices, BIOMED - 2019 Design of Medical Devices Conference, DMD 2019. Frontiers in Biomedical Devices, BIOMED - 2019 Design of Medical Devices Conference, DMD 2019, American Society of Mechanical Engineers (ASME), 2019 Design of Medical Devices Conference, DMD 2019, Minneapolis, United States, 4/15/19. https://doi.org/10.1115/DMD2019-3267

Huang TH, Yang J, Pushaj E, Silvanov V, Yu S, Yang X et al. Soft physiology sensors and machine learning to enhance spinal cord injury and stroke rehabilitation outcomes in home settings. In Frontiers in Biomedical Devices, BIOMED - 2019 Design of Medical Devices Conference, DMD 2019. American Society of Mechanical Engineers (ASME). 2019. (Frontiers in Biomedical Devices, BIOMED - 2019 Design of Medical Devices Conference, DMD 2019). doi: 10.1115/DMD2019-3267

Huang, Tzu Hao ; Yang, Jianfu ; Pushaj, Eljona et al. / Soft physiology sensors and machine learning to enhance spinal cord injury and stroke rehabilitation outcomes in home settings. Frontiers in Biomedical Devices, BIOMED - 2019 Design of Medical Devices Conference, DMD 2019. American Society of Mechanical Engineers (ASME), 2019. (Frontiers in Biomedical Devices, BIOMED - 2019 Design of Medical Devices Conference, DMD 2019).

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title = "Soft physiology sensors and machine learning to enhance spinal cord injury and stroke rehabilitation outcomes in home settings",

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.",

author = "Huang, {Tzu Hao} and Jianfu Yang and Eljona Pushaj and Viktor Silvanov and Shuangyue Yu and Xiaolong Yang and Hao Su and Chang, {Shuo Hsiu} and Gerard Francisco",

note = "Publisher Copyright: Copyright {\textcopyright} 2019 ASME; 2019 Design of Medical Devices Conference, DMD 2019 ; Conference date: 15-04-2019 Through 18-04-2019",

year = "2019",

doi = "10.1115/DMD2019-3267",

language = "English (US)",

series = "Frontiers in Biomedical Devices, BIOMED - 2019 Design of Medical Devices Conference, DMD 2019",

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AB - 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.

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Soft physiology sensors and machine learning to enhance spinal cord injury and stroke rehabilitation outcomes in home settings

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