Authors:
Chanjin Seo
1
;
Masato Sabanai
1
;
Hiroyuki Ogata
2
and
Jun Ohya
1
Affiliations:
1
Department of Modern Mechanical Engineering, Waseda University, 3-4-1, Ookubo, Shinjuku, Tokyo and Japan
;
2
Faculty of Science and Technology, Seikei University, 3-3-1, Kichijoji-kitamachi, Musahino-shi, Tokyo and Japan
Keyword(s):
Running Motion, Unsupervised Learning, Coaching System, Stepwise Skill Improvement.
Related
Ontology
Subjects/Areas/Topics:
Feature Selection and Extraction
;
Pattern Recognition
;
Theory and Methods
Abstract:
To improve running performances, each runner’s skill, such as characteristics and habits, needs to be known, and feedback on the performance should be outputted according to the runner's skill level. In this paper, we propose a new coaching system for detecting the skill of a runner and a method of giving feedback using a sprint motion dataset. Our proposed method calculates an extracted feature to detect the skill using an autoencoder whose middle layer is an LSTM layer; we analyse the feature using hierarchical clustering, and we analyse the human joints that affect the skill. As a result of experiments, five clusters are obtained using hierarchical clustering. This paper clarifies how to detect the skill and to output feedback to achieve a level of performance one step higher than the current level.