A Machine Learning based Approach for Gesture Recognition from Inertial Measurements

The interaction based on gestures has become a prominent approach to interact with electronic devices. In this paper a Machine Learning (ML) based approach to gesture recognition (GR) is illustrated; the proposed tool is freestanding from user, device and device orientation. The tool has been tested on a heterogeneous dataset representative of a typical application of gesture recognition. In the present work two novel ML algorithms based on Sparse Bayesian Learning are tested versus other classification approaches already employed in literature (Support Vector Machine, Relevance Vector Machine, k-Nearest Neighbor, Discriminant Analysis). A second element of novelty is represented by a Principal Component Analysis-based approach, called Pre-PCA, that is shown to enhance gesture recognition with heterogeneous working conditions. Feature extraction techniques are also investigated: a Principal Component Analysis based approach is compared to Frame-Based Description methods.