Domain adaptation for gesture recognition using hidden Markov models

Gesture recognition is becoming popular as an efficient input method for human computer interaction. However, challenges associated with data collection, data annotation, maintaining standardization, and the high variance of data obtained from different users in different environments make developing such systems a difficult task. The purpose of this study is to integrate domain adaptation methods for the problem of gesture recognition. To achieve this task, domain adaptation is performed from hand written digit trajectory data to hand trajectories obtained from depth cameras. The performance of the applied Feature Augmentation method is evaluated through analysis of recognition performance vs percentage of target class samples in training and through the analysis of the transferability of different gestures.