在老年人中使用可穿戴的足部压力和半监督学习来评估平衡和跌倒风险
Jianlin Ou1, Fangting Chen2, Chengqiang Liao3
1Department of Rehabilitation Medicine, The First Affiliated Hospital of Jinan University, Guangzhou, China.
Frontiers in bioengineering and biotechnology
|January 28, 2026
概括
布对老年人来说构成重大公共健康风险. 这项研究引入了智能内和人工智能,通过预测伯格平衡量级得分来客观评估跌倒风险,帮助早期干预.
科学领域:
- 老年学是一门学科.
- 生物医学工程 生物医学工程
- 公共卫生 公共卫生
背景情况:
- 跌倒对老年人来说是一个主要问题,导致残疾和高医疗费用.
- 当前余额评估是主观的,耗时的,限制了社区使用.
- 积极的公共卫生干预需要客观,可扩展的跌倒风险评估.
研究的目的:
- 开发和验证智能鞋类系统,以客观地评估跌倒风险.
- 用脚部压力数据和机器学习来预测伯格平衡量级的得分.
- 为了使老年人早期发现跌倒风险,以便及时进行干预.
主要方法:
- 在136名老年人 (60-90岁) 中收集了智能内 (8个传感器/脚) 的足部压力信号.
- 提取了156个生物力学特征,并应用了多模型错误一致性方法.
- 利用特征选择来识别关键预测因素,并开发了一个预测模型.
主要成果:
- 该模型在验证中达到3.99的根平均平方误差 (RMSE),在独立测试中达到3.13.
- 确定了10个可解释的与压力,强度和对称性相关的生物力学特征.
- 该系统证明了客观和准确的预测伯格平衡量表得分.
结论:
- 一个可穿戴,可解释和可扩展的智能鞋类系统可以客观地评估老年人的跌倒风险.
- 这项技术促进了早期检测和基于社区的干预,以支持健康的衰老.
- 该方法提供了一种实际的解决方案,以改善预防跌倒的公共卫生策略.
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