Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Design and performance evaluation of deep learning-based unmanned medical systems for rehabilitation medical assistance

재활 의료 보조를 위한 딥러닝 기반 무인 의료 시스템의 설계 및 성능평가

  • Choi, Donggyu (Department of Computer Engineering, Dong-Eui University) ;
  • Jang, Jongwook (Department of Computer Engineering, Dong-Eui University)
  • Received : 2021.09.18
  • Accepted : 2021.09.25
  • Published : 2021.12.31
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Abstract

With the recent COVID-19 situation, countries are seriously feeling the need for medical personnel and their technologies. PDepending on the aging society, the number of medical staff is actually decreasing, and in order to solve this problem, research is needed to replace the part that does not require high expertise among actual medical practices performed by doctors. This paper describes and proposes actual research methods related to unmanned medical systems that use various deep learning image processing-based technologies to check the recovery status applicable to rehabilitation areas where medical staff should face patients directly. The proposed method replaces passive calculations such as a protractor or a method of drawing a line in a photograph, which is the method used for actual motion comparison. Since it is performed in real time, it helps to diagnose quickly, and it is easy for medical staff to provide necessary information because data on the degree of match of motion performance can be checked.

최근 코로나 상황을 겪으면서 국가들은 의료인력과 그 기술에 대한 필요성을 심각하게 느끼고 있다. 고령화되고 있는 사회에 따라 실제로 의료진의 수는 줄고 있으며, 이러한 문제점을 해결하기 위해서 실제 의사가 하는 의료 행위 중고도의 전문성을 요구하지 않는 부분을 대체 할 수 있는 연구가 필요하다. 본 논문에서는 존재하는 다양한 딥러닝 영상처리 기반 기술을 활용하여 의료진이 직접 환자를 대면해야 하는 재활 분야에 적용할 수 있는 회복 상태를 확인하는 업무에 관한 무인 의료 시스템과 관련한 실제 연구 방법들을 서술하고 제안한다. 제안하는 방법은 실제 동작 비교에 사용했던 방법인 각도계나 사진에 선을 긋는 방법과 같은 수동적인 계산을 대체한다. 실시간으로 수행하므로 빠른 진단에 도움을 주며, 동작 수행 일치도에 대한 데이터를 확인할 수 있기에 의료진이 필요한 정보를 쉽게 제공한다.

Keywords

Acknowledgement

This research was supported by the BB21plus funded by Busan Metropolitan City and Busan Institute for Talent & Lifelong Education(BIT). And, this research was supported by the MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program(IITP-2021-2020-0-01791) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation). And, this work was supported by Dong-eui University Grant. (202101940001)

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