Motor Skill Development Using Motion Recognition based on an HMM | Zendy

Keita Yamada | Zendy; Kenji Matsuura | Zendy; Keisuke Hamagami | Zendy; Hirofumi Inui | Zendy

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Motor Skill Development Using Motion Recognition based on an HMM

Author(s) -

Keita Yamada,

Kenji Matsuura,

Keisuke Hamagami,

Hirofumi Inui

Publication year - 2013

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2013.09.197

Subject(s) - hidden markov model , computer science , motion (physics) , artificial intelligence , feature (linguistics) , symbol (formal) , speech recognition , sequence (biology) , pattern recognition (psychology) , computer vision , gesture recognition , gesture , philosophy , linguistics , biology , genetics , programming language

The purpose of this study is to propose an original method that enables the recognition of a swinging motion. Furthermore, we apply the method for supporting skill development. In this study, we use an optical-motion capture system to monitor a human's motion. The captured data are transformed into series of symbols by using a feature quantity. The feature quantity is height of hands. The symbol sequence is learned and recognized by a Hidden Markov Model (hereinafter HMM). An evaluation was performed with five subjects. The target motions are the batting and pitching of a baseball and, the motor actions of tennis, such as swinging in service, forehand and backhand. The accuracy of evaluation with the dataset is more than 80% recognition

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