Gesture Recognition Based on Deep Belief Networks

Abstract

Analyzing the data acquired from the inertial sensor in mobile phones has been proved to be an effective way in gesture recognition. This research introduces deep belief networks (DBN) to solve the inertial sensor-based gesture recognition problem and obtains a satisfactory result on the BUAA Mobile Gesture Database. The optimal architecture and the hyper parameters of DBN were tuned according to the performance of experiments in order to get a high recognition accuracy within short time. Besides, three state-of-the-art methods were tested on the same database and the comparison of results indicates that the proposed method achieved a much better recognition accuracy, which considerably improves the recognition performance.