Discovery of skills from motion data

Authors:
Kosuke Makio;Yoshiki Tanaka;Kuniaki Uehara
Affiliations:
Graduate School of Science and Technology, Kobe University, Japan;Graduate School of Science and Technology, Kobe University, Japan;Graduate School of Science and Technology, Kobe University, Japan
Venue:
JSAI'03/JSAI04 Proceedings of the 2003 and 2004 international conference on New frontiers in artificial intelligence
Year:
2003

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Abstract

In this paper, we discuss how to discover "skills" from motion data. Being able to understand how a skilled person moves enables beginners to make better use of their bodies and to become experts easier. However, only few attempts have so far been made for discovering skills from human motion data. To extract skills from motion data, we employ three approaches. As a first approach, we present association rule approach which extracts the dependency among the body parts to find the movement of the body parts performed by the experts. The second is an approach that extracts frequent patterns (motifs) from motion data. Recently, many researchers propose algorithms for discovering motifs. However, these algorithms require that users define the length of the motifs in advance. Our algorithm uses the MDL principle to overcome this problem so as to discover motifs with optimal length. Finally, we compare the motions of skilled tennis players and beginners, and discuss why skilled players can better serve.