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从跑步机到户外地面行走:通过转移学习,提高老年人的地面接触时间检测

Sailee Sansgiri¹, Emmi Matikainen-Tervola², Merja Rantakokko³

¹Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, 40700, Finland.

Experimental gerontology

|February 7, 2026

概括

深度学习模型可以识别移动监控的地面接触时间. 转移学习提高了模型性能,改善了老年人的现实应用.

科学领域:

生物力学生物力学
机器学习机器学习
老年学是指老年学的学科.

背景情况:

准确的地面接触时间 (GCT) 对于评估老年人的移动性至关重要.
基于实验室和跑步机的方法在现实世界的适用性上有局限性.
深度学习为分析可穿戴传感器的步态数据提供了潜力.

研究的目的:

用惯性测量单元 (IMU) 数据评估用于GCT识别的深度学习模型.
评估从年轻成年人的跑步机数据到老年人的跑步机和户外散步的模型概括性.
探索转移学习以提高老年人GCT预测准确度.

主要方法:

在IMU行走数据上训练完全连接的卷积神经网络 (CNN) 和双向LSTM模型.
从跑步机上的年轻成年人和跑步机上的老年人和各种户外地形收集了数据.
通过对老年人的数据进行微调模型的应用转移学习;使用F1分数和MAE进行评估.

主要成果:

CNN模型在跑步机和各种户外步行条件中表现出卓越的性能.
转移学习显著提高了模型准确性,特别是在有限的老年人数据下.
在下降行走中观察到较差的性能,这表明需要专门的算法.

结论:

深度学习,特别是CNN,在现实世界移动监控中显示出准确的GCT识别的前景.
转移学习对于将模型适应新人群和环境是有效的.
需要进一步的研究,以应对复杂地形的挑战,如下降.

关键词:

衰老的衰老衰老的衰老深度学习是一种深度学习.地面接触时间测定户外环境户外环境转移学习转移学习在跑步机上跑步.步行走路走路

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