Adaptation procedure for HMM-based sensor-dependent gesture recognition

In this paper, we address the problem of sensor-dependent gesture recognition thanks to adaptation procedure. Capturing human movements by a motion capture (MoCap) system provides very accurate data. Unfortunately, such systems are very expensive, unlike recent depth sensors, like Microsoft Kinect, which are much cheaper, but provide lower data quality. Hidden Markov Models (HMMs) are widely used in gesture recognition to learn the dynamics of each gesture class. However, models trained on one type of data can only be used on data of the same type. For this reason, we propose to adapt HMMs trained on Mocap data to a small set of Kinect data using Maximum Likelihood Linear Regression (MLLR) to recognize gestures captured by a Kinect. Results show that using this method, we can achieve a recognition average accuracy of 84.48% using a small set of adaptation data while, using the same set to create new models, we obtain only 72.41% of accuracy.