The study of feature vector in HMM-based Wii application for Thai sword dance

Author

Sanpechuda, T. ; Kovavisaruch, L. ; Chinda, K. ; Chaiwongyen, A. ; Wisadsud, S. ; Wongsatho, T. ; Charoenporn, T.

Author_Institution

Nat. Electron. & Comput. Technol. Center, Pathumthani, Thailand

fYear

2011

fDate

7-9 Dec. 2011

Firstpage

1

Lastpage

5

Abstract

In beginning to classify gestures, a Wii remote is used as the primary tool for collecting raw data. Next, the Hidden Markov Model method is used to classify the gestures. The performance of this classification method is reliant on the feature vector used. In this paper, we will propose an appropriate feature vector for classifying gestures used in Thai sword dancing. The feature vectors are evaluated for their accuracy of classification based on their receptivity to acceleration, velocity, and displacement.

Keywords

hidden Markov models; humanities; image classification; HMM-based Wii application; Thai sword dance; acceleration; displacement; feature vector; gesture classification; hidden Markov model method; raw data collection; velocity; Artificial intelligence; Bismuth; Hidden Markov models; Support vector machine classification; Classification; Gesture; HMM; Thai sword dance; Wii remote; feature vector;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Signal Processing and Communications Systems (ISPACS), 2011 International Symposium on

Conference_Location

Chiang Mai

Print_ISBN

978-1-4577-2165-6

Type

conf

DOI

10.1109/ISPACS.2011.6146117

Filename

6146117