We present a method to compute a measure of karate movement quality from MoCap data. We start from well-known common assumptions: an expert athlete is able to perform movements characterized by stable and clean postures and stances, i.e., he is able to conclude the movements without hesitation, noisy small fluctuations or movement ripples. To explore this hypothesis, we collected a dataset of motion capture data of movements of five athletes while performing two different kata's for a total of 22 trials. The athletes have two distinct levels of skill and age: junior brown belt and senior black belt. For each trial, we compute the acceleration of the limbs (arms and legs) and carry out a multi-scale analysis to identify and extract relevant events. Such events correspond to maxima and minima of acceleration intensity (i.e. peaks of high acceleration or deceleration) that occur near the start and the end points of each basic movement segment in a session of kata. Significant events are then selected and an event-synchronisation approach is used to measure the amount of synchrony between the two arms and between the two legs. Results show that expert performers exhibit higher synchronisation with respect to beginners, resulting in more stable and clean movements perceived by observers.
© Copyright 2017 Proceedings of the 4th International Conference on Movement Computing. Julkaistu Tekijä ACM. Kaikki oikeudet pidätetään. / Kääntänyt https://www.DeepL.com/Translator