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

Content Provider	Springer Nature Link
Author	Zhou, Yinghui Cheng, Zixue Jing, Lei
Copyright Year	2014
Abstract	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.
Starting Page	9387
Ending Page	9406
Page Count	20
File Format	PDF
ISSN	13807501
Journal	Multimedia Tools and Applications
Volume Number	74
Issue Number	21
e-ISSN	15737721
Language	English
Publisher	Springer US
Publisher Date	2014-05-28
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Wearable computing Gesture recognition Online segmentation Adaptive threshold One-stroke gestures Multimedia Information Systems Computer Communication Networks Data Structures, Cryptology and Information Theory Special Purpose and Application-Based Systems
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Hardware and Architecture Software Media Technology

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