Using Probabilistic Movement Primitives in Analyzing Human Motion Difference under Transcranial Current Stimulation

TL;DR

This paper introduces the use of probabilistic movement primitives (ProMPs) to model and analyze human motion data, especially under different transcranial current stimulation methods, demonstrating their robustness and effectiveness.

Contribution

It applies ProMPs combined with KL divergence to quantify effects of brain stimulation on human motion, a novel approach in this context.

Findings

ProMPs effectively model human motion trajectories.

ProMPs combined with KL divergence can distinguish effects of different stimulation methods.

Initial results with 10 participants validate the approach.

Abstract

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.