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http://dx.doi.org/10.3745/KTCCS.2022.11.5.157

Implementation of a Transition Rule Model for Automation of Tracking Exercise Progression  

Chung, Daniel (숭실대학교 정보통신소재융합학과)
Ko, Ilju (숭실대학교 글로벌미디어학부)
Publication Information
KIPS Transactions on Computer and Communication Systems / v.11, no.5, 2022 , pp. 157-166 More about this Journal
Abstract
Exercise is necessary for a healthy life, but it is recommended that it be conducted in a non-face-to-face environment in the context of an epidemic such as COVID-19. However, in the existing non-face-to-face exercise content, it is possible to recognize exercise movements, but the process of interpreting and providing feedback information is not automated. Therefore, in this paper, to solve this problem, we propose a method of creating a formalized rule to track the contents of exercise and the motions that constitute it. To make such a rule, first make a rule for the overall exercise content, and then create a tracking rule for the motions that make up the exercise. A motion tracking rule can be created by dividing the motion into steps and defining a key frame pose that divides the steps, and creating a transition rule between states and states represented by the key frame poses. The rules created in this way are premised on the use of posture and motion recognition technology using motion capture equipment, and are used for logical development for automation of application of these technologies. By using the rules proposed in this paper, not only recognizing the motions appearing in the exercise process, but also automating the interpretation of the entire motion process, making it possible to produce more advanced contents such as an artificial intelligence training system. Accordingly, the quality of feedback on the exercise process can be improved.
Keywords
Transition Rule; Movement Tracking; Exercise Progression; Key Frame Postures; Automation;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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