Accurate online alignment of human motor performances

Abstract

Many approaches for motion processing or motion analysis employ Dynamic Time Warping (DTW) for temporally aligning an input movement with a reference movement. DTW, however, does not work online since it requires the complete input trajectory. Its online extension Open-End DTW can lead to poor alignments. In this paper we propose Weight-Optimized Open-End DTW, which combines path-length weighting and joint weights optimized from training data. We demonstrate our method to work online and to outperform Open-End DTW in terms of alignment quality.