Gesture Recognition of Dance using Chain Code and Hidden Markov Model

Joko, Sutopo (2019) Gesture Recognition of Dance using Chain Code and Hidden Markov Model. Gesture Recognition of Dance using Chain Code and Hidden Markov Model, 8 (6). pp. 3194-3199. ISSN 2278-3091

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Abstract

Dance one culture consists of motion. This paper seeks to recognize Golek Menak Dance movement to be easily studied from Indonesia, where the dance of the dancers (actor) is performed by using the motion capture Kinect sensor which then produces motion data format with Biovision Hierarchy (BVH), where data is a tensor which has position x, y, z. This research use test data Jogetan and Sabetan movement carried out featuring by Chain Code 15 (CC-15), which is a combination of 15 directions with forward motion (1), backward (-1) and fixed (0) to obtain vector quantization which is then carried by the gesture recognition using Hidden Markov Model (HMM). The novelty in this paper use Chain Code 15 (CC-15) to conduct the introduction featuring Dance with HMM classification, which produced an accuracy of 90% of ten (10) test data movement. Keywords: dance, featuring, chain code, gesture, HMM

Item Type:	Article
Subjects:	N Fine Arts > NX Arts in general
Divisions:	Fakultas Sains Dan Teknologi > Data Science
Depositing User:	ST., MT. Joko Sutopo
Date Deposited:	04 Apr 2023 09:51
Last Modified:	04 Apr 2023 09:51
URI:	http://eprints.uty.ac.id/id/eprint/12522

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