CNN 기법의 이미지 학습을 통한 팔굽혀펴기 자세 정확도 측정 = Measurement of Push-up Accuracy Using Image Learning by CNN

다국어 초록 (Multilingual 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.

참고문헌 (Reference)

1 성호용, "한국과 미국 육군의 팔굽혀펴기 및 윗몸일으키기 측정형태별 근전도 분석" 화랑대연구소 74 (74): 281-295, 2018

2 김경오, "코로나19 시국 이후 체육교육의 행태와 의미의 변화: 포스트 코로나를 위한 함의" 한국웰니스학회 15 (15): 401-412, 2020

3 안정욱, "웨어러블 동작센서와 인공지능 학습모델기반에서 행동인지의 개선" 한국멀티미디어학회 21 (21): 982-990, 2018

4 S. Albawi, "Understanding of a Convolutional Neural Network" 2017

5 S. Wallelign, "Soyean Plant Disease Identification Using Convolutional Neural Network" 31 : 146-151, 2018

6 L. Taylor, "Improving Deep Learning using Generic Data Augmentation"

7 A. Krizhevsky, "ImageNet Classification with Deep Convolutional Neural Networks" 60 (60): 84-90, 2017

8 S. S. Yadav, "Deep Convolutional Neural Network based Medical Image Classification for Disease Diagnosis" 6 (6): 1-18, 2019

9 박병서, "CNN기반 딥러닝을 이용한 Kuzushiji-MNIST/49 분류의 정확도 향상을 위한 학습 방안" 한국정보통신학회 24 (24): 355-363, 2020

10 강호선, "CNN 기반 주행 중인 차량 간 상대속도 추정 알고리즘" 제어·로봇·시스템학회 27 (27): 37-43, 2021

11 A. Shustanov, "CNN Design for Real-time Traffic Sign Recognition" 201 : 718-725, 2017