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

IF 1.6 3区 心理学 Q4 NEUROSCIENCES Human Movement Science Pub Date : 2024-03-27 DOI:10.1016/j.humov.2024.103212
Chien-Hua Huang , Tien-lung Sun , Min-Chi Chiu , Bih-O Lee
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Abstract

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.

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通过坐立运动中的关键姿势信息预测老年人功能衰退的可行性
背景及早发现日间护理中心老年人的功能衰退有助于及时采取预防和治疗措施。研究问题通过图像识别分析老年人在坐立(STS)动作中的姿势,能否建立预测模型。为了估算姿势关键点信息,我们采用了一个基于区域的卷积神经网络模型,并利用九个关键点及其坐标计算出七个特征值(X1-X7),这些特征值代表了 STS 动作过程中的运动曲线特征。对三组不同能力的人群(大学生、社区居住老人和日间护理中心老人)的四种 STS 策略和四种补偿策略进行了单因素方差分析。结果除 X2(动量传递阶段,p = 0.168)外,其他特征值在参与者组间均存在显著差异(p < 0.05);除 X2(动量传递阶段,p = 0.168)外,其他特征值在参与者组间均存在显著差异(p < 0.05)。STS模式之间除X2(p = 0.219)和X3(髋部上升阶段,p = 0.286)外,所有特征值均有显著差异(p < 0.05);补偿策略之间除X2(p = 0.842)和X3(p = 0.074)外,所有特征值均有显著差异(p < 0.05)。利用七个姿势关键点的运动曲线特征值建立了一个机器学习模型,该模型在能力检测方面的准确率为 85%,在模式检测方面的准确率为 70%,在补偿策略检测方面的准确率为 85%。我们的机器学习模型具有出色的预测准确性,可用于开发廉价而有效的系统,帮助护理人员持续监测老年人的 STS 模式和补偿策略,作为功能衰退的预警信号。
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来源期刊
Human Movement Science
Human Movement Science 医学-神经科学
CiteScore
3.80
自引率
4.80%
发文量
89
审稿时长
42 days
期刊介绍: Human Movement Science provides a medium for publishing disciplinary and multidisciplinary studies on human movement. It brings together psychological, biomechanical and neurophysiological research on the control, organization and learning of human movement, including the perceptual support of movement. The overarching goal of the journal is to publish articles that help advance theoretical understanding of the control and organization of human movement, as well as changes therein as a function of development, learning and rehabilitation. The nature of the research reported may vary from fundamental theoretical or empirical studies to more applied studies in the fields of, for example, sport, dance and rehabilitation with the proviso that all studies have a distinct theoretical bearing. Also, reviews and meta-studies advancing the understanding of human movement are welcome. These aims and scope imply that purely descriptive studies are not acceptable, while methodological articles are only acceptable if the methodology in question opens up new vistas in understanding the control and organization of human movement. The same holds for articles on exercise physiology, which in general are not supported, unless they speak to the control and organization of human movement. In general, it is required that the theoretical message of articles published in Human Movement Science is, to a certain extent, innovative and not dismissible as just "more of the same."
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