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使用IMU在智能手机,手表和耳机中的日常数据上的步态分析

Zhuoli Wang¹, Yuta Sugiura¹

¹Graduate School of Science and Technology, Keio University, 3 Chome-14-1 Hiyoshi, Kohoku Ward, Yokohama, 223-0061, Kanagawa, Japan.

|September 25, 2025

概括

这项研究开发了一种使用智能手机传感器进行步态分析的新算法,在行走和跑步等日常活动的细分方面实现了89.25%的准确性. 该系统可靠地分组不同的步态,即使在不同的智能手机携带位置.

科学领域:

生物力学和可穿戴技术
人类运动分析分析
用于健康监测的信号处理.

背景情况:

智能设备中的惯性测量单元 (IMU) 传感器在实验室之外提供了具有成本效益的步态分析.
分析来自现实世界,日常活动的步态数据仍然存在挑战.
消费级传感器越来越容易用于运动数据收集.

研究的目的:

开发和验证使用消费级IMU设备进行步态分析的新算法.
为了使日常生活中同步步行的可靠细分和分组.
在不同的智能手机携带条件和步态类型中评估算法性能.

主要方法:

16名参与者带着智能手机在口袋或袋子里进行了步行,慢跑和爬楼梯.
来自iPhone,Apple Watch和AirPods Pro的数据被收集,Xsens的运动捕捉作为基本真相.
一个带有连续性匹配得分 (CMS) 的滑动窗口算法用于步态细分和分组.

主要成果:

该算法实现了89.25%的整体细分精度,在口袋手机中达到90.38%的峰值.
步行分组是可靠的,对于背包条件的准确性略有下降.
只有步行 (95.67%) 和只有运行 (96.21%) 的数据集的准确度很高.

结论:

通过使用消费者IMU设备成功引入了一种用于日常生活步态分析的系统.
该算法有效地细分和分组多个同步步行类型.
未来的工作将提高适应动态环境和各种数据集的适应性.

关键词:

步态分析步态分析惯性测量单位 (IMU) 是指惯性测量单位.步骤细分的分段化可穿戴式传感器

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