PolitoBIOMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy.
Sensors (Basel). 2024 Sep 22;24(18):6119. doi: 10.3390/s24186119.
The aim of this contribution is to present a segmentation method for the identification of voluntary movements from inertial data acquired through a single inertial measurement unit placed on the subject's wrist. Inertial data were recorded from 25 healthy subjects while performing 75 consecutive reach-to-grasp movements. The approach herein presented, called DynAMoS, is based on an adaptive thresholding step on the angular velocity norm, followed by a statistics-based post-processing on the movement duration distribution. Post-processing aims at reducing the number of erroneous transitions in the movement segmentation. We assessed the segmentation quality of this method using a stereophotogrammetric system as the gold standard. Two popular methods already presented in the literature were compared to DynAMoS in terms of the number of movements identified, onset and offset mean absolute errors, and movement duration. Moreover, we analyzed the sub-phase durations of the drinking movement to further characterize the task. The results show that the proposed method performs significantly better than the two state-of-the-art approaches (i.e., percentage of erroneous movements = 3%; onset and offset mean absolute error < 0.08 s), suggesting that DynAMoS could make more effective home monitoring applications for assessing the motion improvements of patients following domicile rehabilitation protocols.
本研究旨在提出一种基于放置在受试者腕部的单个惯性测量单元获取的惯性数据识别自愿运动的分割方法。对 25 名健康受试者在连续完成 75 次伸手抓握运动时记录的惯性数据进行分析。本文提出的方法称为 DynAMoS，它基于角速度范数的自适应阈值步骤，然后对运动持续时间分布进行基于统计学的后处理。后处理旨在减少运动分割中的错误转换次数。我们使用立体摄影测量系统作为金标准来评估该方法的分割质量。与 DynAMoS 相比，本文还比较了两种已在文献中提出的流行方法在识别运动数量、起始和结束平均绝对误差以及运动持续时间方面的性能。此外，我们分析了饮水运动的子阶段持续时间，以进一步描述该任务。结果表明，所提出的方法明显优于两种最先进的方法（即错误运动的百分比=3%；起始和结束平均绝对误差<0.08 秒），这表明 DynAMoS 可以更有效地用于家庭监测应用，以评估患者在家居康复方案后运动改善的情况。
