A HMM-based fundamental motion synthesis approach for gesture recognition on a nintendo triaxial accelerometer

In this paper, we show how to use a Nintendo Wiimote triaxial accelerometer as an input device to make a gesture recognition system based on Hidden Markov Model as the kernel recognition algorithm. We adopted a set of basic movements called “Fundamental Motions” as the synthesis units for all the other complex motions. In the preliminary study, we tried to discriminate the digits from `0´ to `9´. We analyzed this task and found a set of 16 motions are appropriate to be used as HMM modeling units. By using appropriate feature extraction and HMM topology, we can achieve near 98% and 65% accuracy for discrete motions and continuous motions, respectively.