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http://dx.doi.org/10.3745/KTCCS.2021.10.6.181

A Movement Tracking Model for Non-Face-to-Face Excercise Contents  

Chung, Daniel (숭실대학교 정보통신소재융합학과)
Cho, Mingu (숭실대학교 글로벌미디어학부)
Ko, Ilju (숭실대학교 글로벌미디어학부)
Publication Information
KIPS Transactions on Computer and Communication Systems / v.10, no.6, 2021 , pp. 181-190 More about this Journal
Abstract
Sports activities conducted by multiple people are difficult to proceed in a situation where a widespread epidemic such as COVID-19 is spreading, and this causes a lack of physical activity in modern people. This problem can be overcome by using online exercise contents, but it is difficult to check detailed postures such as during face-to-face exercise. In this study, we present a model that detects posture and tracks movement using IT system for better non-face-to-face exercise content management. The proposed motion tracking model defines a body model with reference to motion analysis methods widely used in physical education and defines posture and movement accordingly. Using the proposed model, it is possible to recognize and analyze movements used in exercise, know the number of specific movements in the exercise program, and detect whether or not the exercise program is performed. In order to verify the validity of the proposed model, we implemented motion tracking and exercise program tracking programs using Azure Kinect DK, a markerless motion capture device. If the proposed motion tracking model is improved and the performance of the motion capture system is improved, more detailed motion analysis is possible and the number of types of motions can be increased.
Keywords
Non-face-to-face Exercise; Azure Kinect; Motion Capturing; Motion Tracking;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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