一种新的方法来准确地将步行周期划分为七个阶段,使用树角速度
Mikko Salminen1, Jarmo Perttunen2, Janne Avela2
1Faculty of Medicine and Health Technology, Tampere University, Korkeakoulunkatu 3, Tampere 33720, Finland.
Gait & posture
|April 11, 2024
概括
使用累积中侧角速度 (CSAV) 的新方法使用IMU传感器准确检测关键步行事件,如脚 (TO) 和脚相邻 (FA). 这一进步有助于在各种条件下进行全面的步态分析.
科学领域:
- 生物力学 生物力学
- 步态分析 步态分析
- 可穿戴式传感器技术
背景情况:
- 准确的步态事件检测对于评估步态模式和异常至关重要.
- 惯性测量装置 (IMU) 传感器越来越多地用于步态分析,主要用于初始接触 (IC) 和脚脱落 (TO) 事件.
- 综合步态分析需要精确检测额外的事件,如脚跟上升 (HR),脚相邻 (FA),垂直 (TBV).
研究的目的:
- 开发和验证一种基于IMU的新方法来检测HR,TO,FA和TBV步行事件.
- 将新方法的性能与现有的步行事件检测技术进行比较.
主要方法:
- 开发了一种基于IMU的新方法,利用累积中侧角速度 (CSAV) 来检测步行事件.
- 验证是使用大数据集进行的,大约有25,000个健身成年人在不同步行速度和鞋履条件下进行的步行周期.
- 准确性与黄金标准的力板和运动捕捉数据进行了评估,并与现有的TO检测方法进行了比较.
主要成果:
- 该CSAV方法在检测TO,FA和TBV事件方面表现出高准确性,在HR事件方面表现出中度准确性.
- 与现有的TO检测方法相比,这种新方法表现出更高的性能.
- 在不同的步行速度和鞋履条件下,CSAV方法被证明是强大的和稳定的.
结论:
- 已经引入了一种基于IMU的高精度方法 (CSAV) 来检测关键步行事件,使步行周期可以分为七个阶段.
- 在各种场景中CSAV方法的有效性突出了其在步态分析中的各种应用潜力.
- 虽然HR检测可能需要进一步改进,但该方法在检测TO,FA和TBV方面的精度提升了基于IMU的步态事件检测,特别是用于细分摇摆阶段.
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