Threshold selection and adjustment for online segmentation of one-stroke finger gestures using single tri-axial accelerometer

摘要

Online segmentation of continuous gestures is a key issue in accelerometer-based gesture recognition. The challenge is to segment short-duration, ambiguous, and individually different gestures by an accurate method for online applications. In this paper, we propose a threshold-based method for the online segmentation of one-stroke gestures using a single tri-axial accelerometer on a finger. The method first obtains a proper initial threshold based on Bayes decision theory to ensure accurate initial segmentation, and then an adaptation mechanism is designed to automatically adjust the threshold to improve user-dependent accuracy. A dataset of over 1,200 samples from 12 gestures and 25 subjects is created for evaluation. The results show that our method achieves high segmentation precision/recall and high user-dependent adaptability with low computational complexity.