Paper Info
Title
Computer-assisted self-training system for sports exercise using kinects
Abstract
Self-training plays an important role in sports exercise. However, if not under the instruction of a coach, improper training postures can cause serious harm to muscles and ligaments of the body. Hence, the development of computer-assisted self-training systems for sports exercise is a recently emerging research topic. In this paper, we propose a Yoga self-training system, entitled YogaST, which aims at instructing the user/practitioner to perform the asana (Yoga posture) correctly and preventing injury caused by improper postures. Involving professional Yoga training knowledge, YogaST analyzes the practitioner's posture from both front and side views using two Kinects with perpendicular viewing directions and assists him/her in rectifying bad postures. The contour, skeleton, and feature axes of the human body are extracted as posture representation. Then, YogaST analyzes the practitioner's posture and presents visualized instruction for posture rectification so that the practitioner can easily understand how to adjust his/her posture.
Year
DOI
Venue
2013
10.1109/ICMEW.2013.6618307
ICME Workshops
Keywords
Field
DocType
human body contour extraction,human body skeleton extraction,image representation,posture rectification,human body feature axes extraction,perpendicular viewing directions,sports video analysis,yoga self-training system,asana,sports training,sport,yoga posture,feature extraction,computer based training,sports exercise,computer-assisted self-training system,yogast,posture representation,kinect,interactive devices,self-learning,posture analysis,data visualization,games,skeleton,torso,accuracy
Preventing injury,Computer vision,Computer science,Image representation,Artificial intelligence,Self training,Human body
Conference
ISSN
Citations 
PageRank 
2330-7927
3
0.45
References 
Authors
6
6
Name
Order
Citations
PageRank
Hua-Tsung Chen128928.72
Yu-Zhen He281.27
Chien-Li Chou38610.09
Suh-Yin Lee41596319.67
Bao-Shuh Paul Lin57818.71
Jen-Yu Yu610412.13