디지털콘텐츠학회 논문지 (Journal of Digital Contents Society)

  • 제19권9호
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  • Pages.1819-1824
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  • 2018
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  • 1598-2009(pISSN)
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  • 2287-738X(eISSN)
한국디지털콘텐츠학회 (Digital Contents Society)
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사물인터넷 기반의 집중도 및 명상도 검출을 통한 ASMR 콘텐츠 제어 기법

A Control Method of ASMR Contents through Attention and Meditation Detection Based on Internet of Things

  • 김민창 (남서울대학교 정보통신공학과) ;
  • 서정욱 (남서울대학교 정보통신공학과)
  • Kim, Minchang (Department of Information and Communication Engineering, Namseoul University) ;
  • Seo, Jeongwook (Department of Information and Communication Engineering, Namseoul University)
  • 투고 : 2018.09.10
  • 심사 : 2018.09.25
  • 발행 : 2018.09.30
⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 사용자의 스트레스 해소와 주의력 향상에 도움이 될 수 있는 ASMR(autonomous sensory meridian response) 콘텐츠 제어 기법을 제안한다. 제안된 기법은 뇌파 측정 디바이스로부터 EEG(electroencephalography), 집중도, 명상도, 눈 깜빡임 데이터를 측정하고 안드로이드 IoT(internet of things) 앱을 통해 oneM2M 표준을 준용한 IoT 서버 플랫폼으로 전송한다. 서버 플랫폼에 수집된 EEG, 집중도 및 명상도 데이터를 사용하여 사용자의 정신건강상태를 분류하기 위한 SVM(support vector machine) 모델을 생성하고, 이 모델을 통해 분류된 사용자의 정신건강상태와 눈 깜빡임 데이터에 따라 ASMR 콘텐츠를 제어한다. 데이터 사용형태에 따라 SVM 모델을 비교한 결과, 집중도와 명상도 데이터를 사용하는 SVM 모델이 85.7%의 정확도를 나타내었고 이 SVM 모델이 분류한 정신건강상태와 눈 깜빡임 데이터의 변화에 따라 ASMR 콘텐츠 제어 알고리즘이 정상적으로 동작하는 것을 확인하였다.

This paper proposes a control method of ASMR(autonomous sensory meridian response) contents to relieve user's stress and improve his attention. The proposed method measures EEG(electroencephalography), attention, meditation, and eyeblink data from an EEG device and sends them to an oneM2M-compliant IoT(internet of things) server platform through an Android IoT Application. Then a SVM(support vector machine) model is built to classify user's mental health status by using EEG, attention and meditation data collected in the server platform. The ASMR contents are controlled by the mental health status classified by a SVM model and the eyeblink data. When comparing the SVM models according to types of data used, the SVM model with attention and meditation data showed accuracy of 85.7%. It was verified that the proposed control algorithm of ASMR contents properly worked as the mental health status from the SVM model and the eyeblink data changed.

키워드

과제정보

연구 과제 주관 기관 : 남서울대학교

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피인용 문헌

  1. 뇌파데이터에 기반한 맞춤형 수면유도음향의 실시간제어 vol.23, pp.2, 2020, https://doi.org/10.9717/kmms.2020.23.2.204