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Development and Validation of an Artificial Intelligence-Based Motion Analysis System for Upper Extremity Rehabilitation Exercises in Patients with Spinal Cord Injury: A Randomized Controlled Trial

Authors
 Hyun Jong Lee  ;  Seung Mo Jin  ;  Seck Jin Kim  ;  Jea Hak Kim  ;  Hogene Kim  ;  EunKyung Bae  ;  Sun Kook Yoo  ;  Jung Hwan Kim 
Citation
 HEALTHCARE, Vol.12(1) : 7, 2024-01 
Journal Title
HEALTHCARE
Issue Date
2024-01
Keywords
artificial intelligence ; motion feedback system ; rehabilitation ; spinal cord injury
Abstract
In this study, we developed an AI-based real-time motion feedback system for patients with spinal cord injury (SCI) during rehabilitation, aiming to enhance their interest and motivation. The effectiveness of the system in improving upper-limb muscle strength during the Thera band exercises was evaluated. The motion analysis program, including exercise repetition counts and calorie consumption, was developed using MediaPipe, focusing on three key motions (chest press, shoulder press, and arm curl) for upper extremity exercises. The participants with SCI were randomly assigned to the experimental group (EG = 4) or control group (CG = 5), engaging in 1 h sessions three times a week for 8 weeks. Muscle strength tests (chest press, shoulder press, lat pull-down, and arm curl) were performed before and after exercises. Although both groups did not show significant differences, the EG group exhibited increased strength in all measured variables, whereas the CG group showed constant or reduced results. Consequently, the computer program-based system developed in this study could be effective in muscle strengthening. Furthermore, these findings may serve as a valuable foundation for future AI-driven rehabilitation exercise systems.
Files in This Item:
T202400971.pdf Download
DOI
10.3390/healthcare12010007
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers
Yonsei Authors
Yoo, Sun Kook(유선국) ORCID logo https://orcid.org/0000-0002-6032-4686
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/198594
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