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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

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.

Keywords

Healthcare;Fitness;Artificial Intelligence;Deep Running;CNN;RNN

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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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Acknowledgement

Supported by : Hansung University

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