Data Glove-based Personalized Continuous Gesture Segmentation

Abstract

In recent years, gesture recognition based on data gloves has attracted increasing attention as a human-computer interaction (HCI) method that is natural, convenient, stable, robust, easy to recognize, and applicable to various usage environments. This research first proposes an advanced smart data glove that integrates cutting-edge flexible capacitive sensors on the fingertips and a 6-axis IMU on the back of the hand to recognize gestures. Secondly, this study proposes a personalized continuous gesture segmentation (PCGS) model that can adaptively calculate the most appropriate gesture segmenting threshold based on the current user and introduces the multi-sliding window theory and kinematic knowledge to perform personalized gesture segmentation. The accuracy of gesture segmentation can reach 94.3%. The result shows that our PCGS model achieves an average segmentation accuracy of 94.3% and outperforms the state-of-the-art pproaches by 11.2% to 18.5%.