Gesture Recognition using HMM-based Fundamental Motion Synthesis with Implementation on a Wiimote

• Main Authors: Wei Cheng Chen, 陳韋誠
• Other Authors: R. Y. Lyu
• Format: Others
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/18269101288733000450

碩士 === 長庚大學 === 資訊工程學系 === 99 === In this paper, we use the Nintendo Wiimote tri-axial accelerometer as an input device to make a gesture recognition system when using the Hidden Markov Model (HMM) as the recognition algorithm. We use a set of basic movements called “Fundamental Motions” as the synthesis of all the other complex motions. These Fundamental Motions are used as HMM modeling units. In our preliminary study, we use Arabic numerals '0 ' to '9' as the first recognition task. We analyze this task and find a set of 16 motions appropriate to be used as HMM modeling units. The second recognition task is Arabic numerals '10 ' to '99', we also use fundamental motion as main concept, but adding connection signal to represent the voice between models. We found the use of connection signal can increase the recognition rate about 30%. By using appropriate feature extraction and HMM topology, a HMM-Viterbi searching algorithm can achieve near 98% accuracy and 62.26% in average for making ten numbers in a set continuous gesture.