Feasibility of predicting functional decline in the elderly through key posture information during sit-to-stand movement.

Autor: Huang CH; Central Taiwan University of Science and Technology; No.666, Buzih Road, Beitun District, Taichung City 406053, Taiwan, ROC. Electronic address: 108184@ctust.edu.tw., Sun TL; Yuan Ze University; No. 135, Yuandong Rd., Zhongli Dist., Taoyuan City 320315, Taiwan, ROC. Electronic address: tsun@saturn.yzu.edu.tw., Chiu MC; National Chin-Yi University of Technology, No.57, Sec. 2, Zhongshan Rd., Taiping Dist., Taichung 411030, Taiwan, ROC. Electronic address: mcchiu@ncut.edu.tw., Lee BO; Kaohsiung Medical University, 100, Shih-Chuan 1st Road, Kaohsiung 80708, Taiwan. Electronic address: biholee@kmu.edu.tw.
Jazyk: angličtina
Zdroj: Human movement science [Hum Mov Sci] 2024 Jun; Vol. 95, pp. 103212. Date of Electronic Publication: 2024 Mar 27.
DOI: 10.1016/j.humov.2024.103212
Abstrakt: Background: Early detection of functional decline in the elderly in day care centres facilitates timely implementation of preventive and treatment measures.
Research Question: Whether or not a predictive model can be developed by applying image recognition to analyze elderly individuals' posture during the sit-to-stand (STS) manoeuvre.
Methods: We enrolled sixty-six participants (24 males and 42 females) in an observational study design. To estimate posture key point information, we employed a region-based convolutional neural network model and utilized nine key points and their coordinates to calculate seven eigenvalues (X1-X7) that represented the motion curve features during the STS manoeuvre. One-way analysis of variance was performed to evaluate four STS strategies and four types of compensation strategies for three groups with different capacities (college students, community-dwelling elderly, and day care center elderly). Finally, a machine learning predictive model was established.
Results: Significant differences (p < 0.05) were observed in all eigenvalues except X2 (momentum transfer phase, p = 0.168) between participant groups; significant differences (p < 0.05) were observed in all eigenvalues except X2 (p = 0.219) and X3 (hip-rising phase, p = 0.286) between STS patterns; significant differences (p < 0.05) were observed in all eigenvalues except X2 (p = 0.842) and X3 (p = 0.074) between compensation strategies. The motion curve eigenvalues of the seven posture key points were used to build a machine learning model with 85% accuracy in capacity detection, 70% accuracy in pattern detection, and 85% accuracy in compensation strategy detection.
Significance: This study preliminarily demonstrates that eigenvalues can be used to detect STS patterns and compensation strategies adopted by individuals with different capacities. Our machine learning model has excellent predictive accuracy and may be used to develop inexpensive and effective systems to help caregivers to continuously monitor STS patterns and compensation strategies of elderly individuals as warning signs of functional decline.
(Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
Externí odkaz: