22IR1800, Development of Intelligent Personalized Rehabilitation Service Technology,
Kim Jae Chul
Abstract
재활운동은 환자별로 운동 종류와 강도 등을 주의 깊게 선택하여 처방 및 수행하여야 한다. 재활운동이 잘못 처방될 경우 그 효과가 미비하거나, 역효과를 내고 2차부상 등 위험한 상황을 초래할 수 있다. 본 연구에서는 세종충남대병원에서 수행 중인 재활운동 중재 임상시험 데이터를 대상으로 개인맞춤형 재활운동 추천 시스템을 개발하였다. 어깨 유착성 관절낭염 및 회전근개 손상 환자와 허리 통증 환자를 대상으로 수행 중인 재활운동 중재 임상시험 데이터로부터 학습 데이터셋을 생성하고, NCF의 NeuMF 모델을 기반으로 재활운동 추천 시스템을 개발하여 그 성능을 검증하였다. 검증을 통해 개발된 재활운동 추천 모델이 GMF 및 MLP 모델 기반의 방식에 비해 보다 우수한 성능을 보임을 확인하였다. 본 연구의 개인맞춤형 재활운동 추천 시스템은 재활환자의 기능회복 및 건강증진과 빠르고 안전한 사회복귀를 도울 수 있을 것으로 기대된다.
KSP Keywords
Collaborative filtering(CF)
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