基于生物力学特征与可穿戴设备的肌少症筛查技术研究进展

doi:10.3969/j.issn.1003-9198.2026.05.019

摘要/Abstract

摘要： 本文综述了肌少症筛查技术的研究进展,重点探讨了基于下肢肌力、足底压力等生物力学指标的新型评估方法,以及可穿戴设备与机器学习技术在该领域的应用潜力。可穿戴传感技术的发展,使得在自然状态下连续、客观地采集步态、平衡和足底压力数据成为可能。这些设备结合机器学习算法,能够从多维度数据中挖掘与肌少症相关的特征模式,构建高精度的风险预测模型。未来研究应致力于优化传感器设计、开发更稳健和可解释的算法模型,并开展多中心临床研究,以推动肌少症筛查技术向家庭化、社区化及个性化方向发展,最终实现对肌少症的早期识别、动态监测和有效干预,改善老年人群的生活质量与健康结局。

关键词: 肌少症, 下肢肌力, 足底压力, 可穿戴设备, 机器学习

Abstract: This review summarizes research advances in sarcopenia screening technologies, focusing on novel assessment methods based on biomechanical indicators such as lower limb muscle strength and plantar pressure, as well as the application potential of wearable devices and machine learning technologies in this field. Advancements in wearable sensor technology have made it possible to continuously and Objectively collect gait, balance, and plantar pressure data in natural conditions. By integrating machine learning algorithms, these devices can extract characteristic patterns associated with sarcopenia from multidimensional data to construct highly accurate risk prediction models. Future research should focus on optimizing sensor design, developing more robust and interpretable algorithmic models, and conducting multicenter clinical studies to advance sarcopenia screening technologies toward home-based, community-based, and personalized applications. These developments will ultimately enable early identification, dynamic monitoring, and effective intervention of sarcopenia, thereby improving the quality of life and health outcomes for the elderly population.

Key words: sarcopenia, lower limb muscle strength, plantar pressure, wearable devices, machine learning

中图分类号:

R 685

陈奕筱, 廖华龙, 林泰平, 岳冀蓉. 基于生物力学特征与可穿戴设备的肌少症筛查技术研究进展[J]. 实用老年医学, 2026, 40(5): 530-535.

CHEN Yixiao, LIAO Hualong, LIN Taiping, YUE Jirong. Research progress on sarcopenia screening based on biomechanical characteristics and wearable devices[J]. Practical Geriatrics, 2026, 40(5): 530-535.

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