Gait Analysis in Powered Exoskeleton-Assisted Walking in Patients with Stroke: A Case Series Cohort

Jian Jia Huang; Shih Chieh Chang; Cheng Hsu Cheng; Timothy Wan; Yu Cheng Pei

doi:10.1109/APSIPAASC58517.2023.10317529

Gait Analysis in Powered Exoskeleton-Assisted Walking in Patients with Stroke: A Case Series Cohort

Jian Jia Huang^*
, Shih Chieh Chang
, Cheng Hsu Cheng
, Timothy Wan
, Yu Cheng Pei

^*Corresponding author for this work

School of Medicine

Chang Gung Memorial Hospital
Chang Gung University
University of Texas at Austin

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

Abstract

This case series cohort study investigated gait characteristics based on sensor data obtained from personalized active powered lower limb exoskeletal robot-assisted gait training and assessed its putative therapeutic effects in patients with stroke. Preliminary findings suggest that the application of the exoskeletal robot did not improve gait symmetry under robot assistance and could facilitate inter-joint coordination within the hip-knee joint movement on the affected side. These findings suggest that robot-assisted gait training could improve gait by guiding motor learning.

Original language	English
Title of host publication	2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	187-194
Number of pages	8
ISBN (Electronic)	9798350300673
DOIs	https://doi.org/10.1109/APSIPAASC58517.2023.10317529
State	Published - 2023
Event	2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023 - Taipei, Taiwan Duration: 31 10 2023 → 03 11 2023

Publication series

Name	2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023

Conference

Conference	2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
Country/Territory	Taiwan
City	Taipei
Period	31/10/23 → 03/11/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Access to Document

10.1109/APSIPAASC58517.2023.10317529

Cite this

Huang, J. J., Chang, S. C., Cheng, C. H., Wan, T., & Pei, Y. C. (2023). Gait Analysis in Powered Exoskeleton-Assisted Walking in Patients with Stroke: A Case Series Cohort. In 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023 (pp. 187-194). (2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APSIPAASC58517.2023.10317529

Huang, Jian Jia ; Chang, Shih Chieh ; Cheng, Cheng Hsu et al. / Gait Analysis in Powered Exoskeleton-Assisted Walking in Patients with Stroke : A Case Series Cohort. 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 187-194 (2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023).

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title = "Gait Analysis in Powered Exoskeleton-Assisted Walking in Patients with Stroke: A Case Series Cohort",

abstract = "This case series cohort study investigated gait characteristics based on sensor data obtained from personalized active powered lower limb exoskeletal robot-assisted gait training and assessed its putative therapeutic effects in patients with stroke. Preliminary findings suggest that the application of the exoskeletal robot did not improve gait symmetry under robot assistance and could facilitate inter-joint coordination within the hip-knee joint movement on the affected side. These findings suggest that robot-assisted gait training could improve gait by guiding motor learning.",

author = "Huang, \{Jian Jia\} and Chang, \{Shih Chieh\} and Cheng, \{Cheng Hsu\} and Timothy Wan and Pei, \{Yu Cheng\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023 ; Conference date: 31-10-2023 Through 03-11-2023",

year = "2023",

doi = "10.1109/APSIPAASC58517.2023.10317529",

language = "英语",

series = "2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "187--194",

booktitle = "2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023",

address = "美国",

}

Huang, JJ, Chang, SC, Cheng, CH, Wan, T & Pei, YC 2023, Gait Analysis in Powered Exoskeleton-Assisted Walking in Patients with Stroke: A Case Series Cohort. in 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023. 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023, Institute of Electrical and Electronics Engineers Inc., pp. 187-194, 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023, Taipei, Taiwan, 31/10/23. https://doi.org/10.1109/APSIPAASC58517.2023.10317529

Gait Analysis in Powered Exoskeleton-Assisted Walking in Patients with Stroke: A Case Series Cohort. / Huang, Jian Jia; Chang, Shih Chieh; Cheng, Cheng Hsu et al.
2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 187-194 (2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023).

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

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AU - Chang, Shih Chieh

AU - Cheng, Cheng Hsu

AU - Wan, Timothy

AU - Pei, Yu Cheng

PY - 2023

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AB - This case series cohort study investigated gait characteristics based on sensor data obtained from personalized active powered lower limb exoskeletal robot-assisted gait training and assessed its putative therapeutic effects in patients with stroke. Preliminary findings suggest that the application of the exoskeletal robot did not improve gait symmetry under robot assistance and could facilitate inter-joint coordination within the hip-knee joint movement on the affected side. These findings suggest that robot-assisted gait training could improve gait by guiding motor learning.

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DO - 10.1109/APSIPAASC58517.2023.10317529

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PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 31 October 2023 through 3 November 2023

ER -

Huang JJ, Chang SC, Cheng CH, Wan T, Pei YC. Gait Analysis in Powered Exoskeleton-Assisted Walking in Patients with Stroke: A Case Series Cohort. In 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 187-194. (2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023). doi: 10.1109/APSIPAASC58517.2023.10317529

Gait Analysis in Powered Exoskeleton-Assisted Walking in Patients with Stroke: A Case Series Cohort

Abstract

Publication series

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Bibliographical note

UN SDGs

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