Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
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Presenting Direction for the Implementation of Personal Movement Trainer through Artificial Intelligence based Behavior Recognition

인공지능 기반의 행동인식을 통한 개인 운동 트레이너 구현의 방향성 제시

  • Ha, Tae Yong (Dept. of Smart Convergence Consulting, Hansung University) ;
  • Lee, Hoojin (Dept. of Smart Convergence Consulting, Hansung University)
  • 하태용 (한성대학교 스마트융합컨설팅학과) ;
  • 이후진 (한성대학교 스마트융합컨설팅학과)
  • Received : 2019.03.27
  • Accepted : 2019.06.20
  • Published : 2019.06.28
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Abstract

Recently, the use of artificial intelligence technology including deep learning has become active in various fields. In particular, several algorithms showing superior performance in object recognition and detection based on deep learning technology have been presented. In this paper, we propose the proper direction for the implementation of mobile healthcare application that user's convenience is effectively reflected. By effectively analyzing the current state of use satisfaction research for the existing fitness applications and the current status of mobile healthcare applications, we attempt to secure survival and superiority in the fitness application market, and, at the same time, to maintain and expand the existing user base.

최근 딥러닝을 비롯한 인공지능 기술의 활용이 다양한 분야에서 활발해지고 있으며, 특히 딥러닝 기술 기반의 객체 인식 및 검출에 뛰어난 성능을 보이는 여러 알고리즘들이 발표되고 있다. 이에 본 논문에서는 사용자의 편의성이 효과적으로 반영된 모바일 헬스케어 애플리케이션 구현에 대한 적절한 방향성을 제시하고자 한다. 기존의 피트니스 애플리케이션들에 대한 이용 만족도 연구 및 모바일 헬스케어 애플리케이션에 대한 현황을 파악하여, 이로부터 피트니스 애플리케이션 시장에서의 생존과 우위를 확보하는 동시에, 최근 주목 받고 있는 인공지능 기술의 효과적인 적용에 의한 성능 개선을 통해 기존 이용자 유지 및 확대를 도모하고자 한다.

Keywords

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Fig. 1. Fitness Apps[4]

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Fig. 2. Deep learning, Machine learning, ArtificialIntel ligence Comparison[8]

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Fig. 3. Structure of Convolutional Neural Network[14]

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Fig. 4. RNN Structure[15]

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Fig. 5. LSTM Cell Structure[16]

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Fig. 6. Distribution of Confidence Values[19]

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Fig. 7. Body Keypoint Localization[20]

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Fig. 8. Research Model

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Fig. 9. Detection Using Confidence Maps

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Fig. 10. Association Using Part Affinity Fields

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Fig. 11. PCK 0.2

Table 1. Classification Of Mobile Healthcare by Type

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Table 2. Healthcare Application Functional Classification

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Table 3. System Requirements

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