In medical tasks such as human motion analysis, computer-aided auxiliary systems have become preferred choice for human experts for its high efficiency. However, conventional approaches are typically based on user-defined features such as movement onset times, peak velocities, motion vectors or frequency domain analyses. Such approaches entail careful data post-processing or specific domain knowledge to achieve a meaningful feature extraction. Besides, they are prone to noise and the manual-defined features could hardly be re-used for other analyses. In this paper, we proposed probabilistic movement primitives (ProMPs), a widely-used approach in robot skill learning, to model human motions. The benefit of ProMPs is that the features are directly learned from the data and ProMPs can capture important features describing the trajectory shape, which can easily be extended to other tasks. Distinct from previous research, where classification tasks are mostly investigated, we applied ProMPs together with a variant of Kullback-Leibler (KL) divergence to quantify the effect of different transcranial current stimulation methods on human motions. We presented an initial result with 10 participants. The results validate ProMPs as a robust and effective feature extractor for human motions.
翻译:在诸如人类运动分析等医疗任务中,计算机辅助系统因其高效率而成为人类专家的首选选择,然而,常规方法通常以用户定义的特点为基础,例如运动开始时间、峰值速度、运动矢量或频率域分析;这类方法需要谨慎的数据处理后或特定领域知识,以便实现有意义的特征提取;此外,它们容易产生噪音,手工界定的特征很难再用于其他分析;在本文件中,我们提议了概率移动原始(ProMPs),这是在机器人技能学习、模拟人类运动方面广泛使用的一种方法;ProMPs的好处是,这些特征是直接从数据中学习,而ProMPs能够捕捉到描述轨迹形状的重要特征,这些特征很容易扩展到其他任务。不同于以往的研究,在对分类任务进行的大部分调查中,我们应用ProMPs和Kullback-Leeperr(KL)的变式来量化不同转基因当前刺激方法对人类运动的影响。我们向10名参与者介绍了初步结果。