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http://dx.doi.org/10.9717/kmms.2021.24.6.805

Measurement of Push-up Accuracy Using Image Learning by CNN  

Lee, Junseok (Dept. of Computer Science, Korea army academy at Yeoncheon)
Oh, Donghan (Dept. of Computer Science, Korea army academy at Yeoncheon)
Ahn, Kyung-Il (Dept. of Physical Education, Korea army academy at Yeoncheon)
Publication Information
Journal of Korea Multimedia Society / v.24, no.6, 2021 , pp. 805-814 More about this Journal
Abstract
Push-ups are one of the body exercises that can be easily measured anytime, anywhere. As one of the most widely used techniques as a test tool for evaluating physical strength, they are broadly used in various fields, especially in fields that require physical ability to estimate, such as military, police, and firefighters. However, social distancing is currently being implemented, and the issue of fairness has been steadily raised due to subtle differences between measurement. Accordingly, in this paper, the correct posture for each individual was photographed and learned by a high-performance computer, and the result was derived by comparing it with the case of performing the incorrect posture of the individual. If method is introduced into the physical fitness evaluation through the proposed method, the individual takes the correct posture and learns the photographed photo, and measures the posture with several images taken during a given time. Through this, it is possible to measure more objectively because it measures with the merit that can be measured even in the present situation and with one's correct posture.
Keywords
Image processing; Convolution Neural Network; Posture Measurement; Push-up; Machine Learning;
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