Using Probabilistic Movement Primitives in Analyzing Human Motion Differences Under Transcranial Current Stimulation.

ABSTRACT: In medical tasks such as human motion analysis, computer-aided auxiliary systems have become the preferred choice for human experts for their 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 ProM

SUBMITTER: Xue H 

PROVIDER: S-EPMC8476753 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature