Lien Wei-Chih, Wang Wen-Fong, Chang Chien-Hsiang, Liu Bo, Yang Yi-Ching, Yang Tai-Hua, Kuan Ta-Shen, Wang Wei-Ming, Huang Wei, Shahmirzadi Danyal, Lin Yang-Cheng
Department of Physical Medicine and Rehabilitation, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
Department of Physical Medicine and Rehabilitation, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
Comput Struct Biotechnol J. 2025 May 28;28:199-210. doi: 10.1016/j.csbj.2025.05.011. eCollection 2025.
Frailty in older adults is caused by functional declines that result in unstable gait. This study analyzed gait in 24 frail and 22 non-frail older adults using acceleration and angular velocity signals from a wireless tri-axial inertial measurement unit (IMU). After noise was removed through Savitzky-Golay and Butterworth filters, gait features correlated with frailty were proposed and evaluated through normality tests and statistical analysis. To evaluate the frailty of older adults based on significant gait features derived from statistical analysis, the primary accuracy achieved is roughly around 84-89 % in k-nearest neighbor, support vector machine, and random forest models. To provide clinicians with a good tool for monitoring frailty and support preventive healthcare and aging-in-place strategies, we propose a gait-based detection system with an optimal feature extraction scheme that can exhaustively enumerate and evaluate potential parameters for optimal performance. This system significantly improved classification metrics (nearly all >95 %) with lower sensitivity and specificity and achieved 96 % accuracy with a portable, low-cost system that uses only one minute of walking data. These findings demonstrate that IMU-based gait analysis improves objectivity and accuracy in frailty classification. The optimal feature extraction scheme further refines performance, offering a scalable and time-efficient solution for community-based frailty detection. This approach highlights the potential of wearable sensors in improving geriatric health assessments.
老年人的虚弱是由导致步态不稳的功能衰退引起的。本研究使用来自无线三轴惯性测量单元（IMU）的加速度和角速度信号，分析了24名虚弱老年人和22名非虚弱老年人的步态。通过Savitzky-Golay滤波器和巴特沃斯滤波器去除噪声后，提出了与虚弱相关的步态特征，并通过正态性检验和统计分析进行评估。为了基于统计分析得出的显著步态特征评估老年人的虚弱程度，在k近邻、支持向量机和随机森林模型中，实现的主要准确率约为84%-89%。为了为临床医生提供一个监测虚弱的良好工具，并支持预防性医疗保健和就地养老策略，我们提出了一种基于步态的检测系统，该系统具有最优特征提取方案，能够详尽地列举和评估潜在参数以实现最优性能。该系统以较低的灵敏度和特异性显著提高了分类指标（几乎所有指标>95%），并通过一个仅使用一分钟步行数据的便携式低成本系统实现了96%的准确率。这些发现表明，基于IMU的步态分析提高了虚弱分类的客观性和准确性。最优特征提取方案进一步优化了性能，为基于社区的虚弱检测提供了一种可扩展且高效的解决方案。这种方法突出了可穿戴传感器在改善老年健康评估方面的潜力。
