传感器特征输入对跨简单运动的关节角度预测的影响
David Hollinger1, Mark C Schall2, Howard Chen3
1Department of Mechanical Engineering, Auburn University, Auburn, AL 36849, USA.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
增加更多的惯性测量单位 (IMU) 或将它们放置在不相邻的身体部位上并没有提高人类运动意图预测 (HMIP) 的准确性. 传感器数量和位置对使用机器学习预测关节角度的影响最小.
科学领域:
- 生物力学 生物力学
- 可穿戴技术可穿戴技术
- 医疗保健中的机器学习
背景情况:
- 像惯性测量单元 (IMU) 这样的可穿戴传感器在机器学习中越来越多地用于健康方面的人类意图识别.
- 关于IMU的数量和位置如何影响人类移动意图预测 (HMIP) 在联合层面的研究有限.
研究的目的:
- 分析IMU数量和位置对简单人体运动预测关节角度准确性的影响.
- 确定最佳的传感器配置,以最大限度地提高机器学习在HMIP中的预测准确度.
主要方法:
- 训练了一种随机森林算法,以使用IMU输入信号的各种组合来预测未来的关节角度.
- 在不同的IMU配置下,对脚,膝盖和关节的评估预测准确性 (RMSE).
主要成果:
- 添加相邻的IMU并没有显著改善关节角度预测的准确性 (例如,脚RMSE 1.92°与3.32°).
- 包括非相邻的IMU也未能提高预测准确性 (例如,脚RMSE5.35°与5.55°).
- 在简单的移动过程中,IMU的数量和位置对预测未来的关节角度的影响最小.
结论:
- 添加IMU,无论是相邻的还是非相邻的,都不会大大提高预测未来联合角度的准确性.
- 目前的关节角度输入足以预测简单的运动,额外的IMU提供有限的预测效益.
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