Inverse kinematics and gesture pattern recognition using Hidden Markov Model on BeatMe! project: Traditional dance digitalization

2015 ◽  
Author(s):  
Zahrotul Aisyah Ulfah ◽  
Aciek Ida Wuryandari ◽  
Yoga Priyana
Keyword(s):  
Pattern Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Inverse Kinematics ◽  
Hidden Markov ◽  
Traditional Dance

Modified student's t ‐hidden Markov model for pattern recognition and classification

2013 ◽  
Vol 7 (3) ◽  
pp. 219-227 ◽  
Author(s):  
Hui Zhang ◽  
Qing Ming Jonathan Wu ◽  
Thanh Minh Nguyen
Keyword(s):  
Pattern Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Student’S T

DANCE MODELLING, LEARNING AND RECOGNITION SYSTEM OF ACEH TRADITIONAL DANCE BASED ON HIDDEN MARKOV MODEL

2015 ◽  
Vol 78 (2-2) ◽  
Author(s):  
Nurfitri Anbarsanti ◽  
Ary S. Prihatmanto
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Recognition System ◽  
Kinect Sensor ◽  
Range Of Movement ◽  
Classifier System ◽  
Traditional Dance ◽  
Noisy Input ◽  
Discrete States

The whole dance of Likok Pulo are modeled by hidden markov model. Dance gestures are cast as hidden discrete states and phrase as a sequence of gestures. For robustness under noisy input of Kinect sensor, an angular representation of the skeleton is designed. A pose of dance is defined by this angular skeleton representation which has been quantified based on range of movement. One unique gesture of dance is defined by sequence of pose and learned and classified by HMM model. Six of dance's gesture classes from the phrase "Assalamualaikum" has been trained with hundreds of gesture instances recorded by the Kinect sensor which performed by three of subjects for each gesture class. The classifier system classify the input testing gesture into one of six classes of predefined gesture or one class of undefined gesture. The classifier system has an accuracy of 94.87% for single gesture.

Fuzzy Observable Markov Models for Pattern Recognition

2007 ◽  
Vol 11 (6) ◽  
pp. 662-667
Author(s):  
Dat Tran ◽  
◽  
Wanli Ma ◽  
Dharmendra Sharma
Keyword(s):  
Pattern Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Vector Quantization ◽  
Markov Models ◽  
Hidden Markov ◽  
Gaussian Mixture ◽  
Written Language ◽  
Mathematical Framework ◽  
Fuzzy Observable

This paper presents a mathematical framework for fuzzy discrete and continuous observable Markov models (OMMs) and their applications to written language, spam email and typist recognition. Experimental results show that the proposed OMMs are more effective than models such as vector quantization, Gaussian mixture model and hidden Markov model.

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