Mobile Information Systems
Volume 2016, Article ID 9849720, 14 pages
http://dx.doi.org/10.1155/2016/9849720
Arm Motion Recognition and Exercise Coaching System for Remote Interaction
School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
Received 28 October 2015; Revised 29 December 2015; Accepted 3 January 2016
Academic Editor: Ondrej Krejcar
Copyright © 2016 Hong Zeng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Arm motion recognition and its related applications have become a promising human computer interaction modal due to the rapid integration of numerical sensors in modern mobile-phones. We implement a mobile-phone-based arm motion recognition and exercise coaching system that can help people carrying mobile-phones to do body exercising anywhere at any time, especially for the persons that have very limited spare time and are constantly traveling across cities. We first design improved k-means algorithm to cluster the collecting 3-axis acceleration and gyroscope data of person actions into basic motions. A learning method based on Hidden Markov Model is then designed to classify and recognize continuous arm motions of both learners and coaches, which also measures the action similarities between the persons. We implement the system on MIUI 2S mobile-phone and evaluate the system performance and its accuracy of recognition.