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基于惯性传感器的步骤检测算法的验证,用于边缘设备部署
Maksymilian Kisiel1, Arslan Amjad1, Agnieszka Szczęsna1
1Department of Computer Graphics, Vision and Digital Systems, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, 44-100 Gliwice, Poland.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
这项研究比较了使用惯性测量单位 (IMU) 的五步检测算法. 峰值检测在整体上表现最好,而光谱分析在楼梯上表现出色,为边缘计算和活动监控提供了洞察力.
科学领域:
- 可穿戴技术可穿戴技术
- 生物医学工程 生物医学工程
- 信号处理 信号处理
背景情况:
- 使用惯性测量单位 (IMU) 的步骤检测对于人类活动识别,室内导航和健康监测至关重要.
- 边缘设备的实现需要高效和准确的步骤检测算法.
研究的目的:
- 验证和比较五种不同的步骤检测算法,以确定边缘设备的适用性.
- 在各种活动和传感器放置中评估算法性能.
主要方法:
- 开发了一个带有两个IMU传感器的Raspberry Pi Pico 2W系统.
- 实施并测试了峰值检测,零交叉,光谱分析,自适应值和SHOE算法.
- 在七个场景 (例如,步行,慢跑,楼梯) 和四个传感器位置 (大腿,脚,手腕,上臂) 中得到验证.
主要成果:
- 峰值检测实现了最高的F1总体得分 (0.82).
- 光谱分析显示,在爬楼梯的场景中表现优越 (F1 = 0.86-0.92).
- 上臂传感器的位置出乎意料地产生了最高的精度 (F1 = 0.84).
结论:
- 建议在边缘设备上使用峰值检测进行一般步骤检测.
- 算法选择应考虑特定活动,特别是爬楼梯.
- 需要进一步的研究来解决非连续步行场景中的算法限制.
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