Nonlinear methods to quantify Movement Variability in Human-Humanoid Interaction Activities

Miguel P. Xochicale, Chris Baber
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Abstract

Human movement variability arises from the process of mastering redundant (bio)mechanical degrees of freedom to successfully accomplish any given motor task where flexibility and stability of many possible joint combinations helps to adapt to environment conditions. While the analysis of movement of variability is becoming increasingly popular as a diagnostic tool or skill performance evaluation, there are remain challenges on applying the most appropriate methods. We therefore investigate nonlinear methods such as reconstructed state space (RSSs), uniform time-delay embedding, recurrence plots (RPs) and recurrence quantification analysis (RQAs) with real-world time-series data of wearable inertial sensors. That said, twenty healthy participants imitated vertical and horizontal arm movements in normal and faster velocity from an humanoid robot. We applied nonlinear methods to the collected data to found visual differences in the patterns of RSSs and RPs and statistical differences with RQAs. We conclude that Shannon Entropy with RQA is a robust method that helps to quantify activities, types of sensors, windows lengths and level of smoothness. Hence this work might enhance the development of better diagnostic tools for applications in rehabilitation and sport science for skill performance or new forms of human-humanoid interaction for quantification of movement adaptations and motor pathologies.
非线性方法量化人-人形交互活动中的运动变异性
人类运动的可变性源于掌握冗余(生物)机械自由度的过程,以成功完成任何给定的运动任务,其中许多可能的关节组合的灵活性和稳定性有助于适应环境条件。虽然变异性运动分析作为一种诊断工具或技能绩效评估越来越受欢迎,但在应用最合适的方法方面仍然存在挑战。因此,我们研究了基于可穿戴惯性传感器真实时间序列数据的非线性方法,如重构状态空间(rss)、均匀时延嵌入、递归图(rp)和递归量化分析(rqa)。也就是说,20名健康的参与者以正常和更快的速度模仿人形机器人的垂直和水平手臂运动。我们对收集的数据应用非线性方法来发现rss和rp模式的视觉差异以及与rqa的统计差异。我们得出结论,香农熵与RQA是一个强大的方法,有助于量化活动,传感器类型,窗口长度和平滑程度。因此,这项工作可能会促进更好的诊断工具的发展,应用于康复和运动科学的技能表现或新的形式的人-类人互动,以量化运动适应和运动病理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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