스마트폰 센서를 통한 우울증 탐지 및 위급상황 탐지 모델 연구
Research on depression and emergency detection model using smartphone sensors
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초록
코로나19의 심화로 인해 고강도 사회적 거리두기가 장기화되고 많은 사회적 문제를 나았다. 특히 비대면 체계로 인해 물리적, 심리적 고립이 발생하였고 많은 피해가 발생하였다. 코로나로 인한 여러 사회적 문제들은 코로나19로 피해를 입은 모든 사람들에게 심한 스트레스로 작용하여 결국엔 우울증 등의 정신건강을 위협하는 요소로 작용되었다. 정신질환을 겪는 사람들은 늘어나는 반면 실제로 정신건강 서비스 이용률은 저조한 상황이기 때문에 정신건강 문제를 겪고 있는 사람들을 위한 시스템 구축이 필요한 상황이다. 이에 본 연구에서는 우울증 대상자와 일반인 대상자로부터 스마트폰을 이용한 센서 정보를 바탕으로 우울증 탐지 및 위급상황 탐지모델을 구축하였다. 우울증 탐지와 위급상황 탐지를 위해 VAE, DAGMM, ECOD, COPOD, LGBM 알고리즘을 이용하였다. 연구 결과로 우울증 탐지 모델은 F1 score 0.93, 위급상황 탐지 모델은 F1 score 0.99의 성능의 모델을 구축하였다.
Abstract
Due to the deepening of COVID-19, high-intensity social distancing has been prolonged and many social problems have been cured. In particular, physical and psychological isolation occurred due to the non-face-to-face system and a lot of damage occurred. The various social problems caused by Corona acted as severe stress for all those affected by Corona 19, and eventually acted as a factor threatening mental health such as depression. While the number of people suffering from mental illness is increasing, the actual use of mental health services is low. Therefore, it is necessary to establish a system for people suffering from mental health problems. Therefore, in this study, depression detection and emergency detection models were constructed based on sensor information using smartphones from depressed subjects and general subjects. For the detection of depression and emergencies, VAE, DAGMM, ECOD, COPOD, and LGBM algorithms were used. As a result of the study, the depression detection model had an F1 score of 0.93 and the emergency situation detection model had an F1 score of 0.99. direction.
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