Journal of Sensor Science and Technology (센서학회지)

The Korean Sensors Society (한국센서학회)
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Smart Helmet for Vital Sign-Based Heatstroke Detection Using Support Vector Machine

SVM 이용한 다중 생체신호기반 온열질환 감지 스마트 안전모 개발

  • Jaemin, Jang (School of Mechanical Engineering, Kyungpook National Unversity) ;
  • Kang-Ho, Lee (Department of medical device, Korea Institute of Machinery and Materials) ;
  • Subin, Joo (Department of medical robotics, Korea Institute of Machinery and Materials) ;
  • Ohwon, Kwon (Department of medical device, Korea Institute of Machinery and Materials) ;
  • Hak, Yi (School of Mechanical Engineering, Kyungpook National Unversity) ;
  • Dongkyu, Lee (Department of medical device, Korea Institute of Machinery and Materials)
  • 장재민 (경북대학교 기계공학부) ;
  • 이강호 (한국기계연구원 의료기계연구실) ;
  • 주수빈 (한국기계연구원의료로봇연구실) ;
  • 권오원 (한국기계연구원 의료기계연구실) ;
  • 이학 (경북대학교 기계공학부) ;
  • 이동규 (한국기계연구원 의료기계연구실)
  • Received : 2022.10.28
  • Accepted : 2022.11.15
  • Published : 2022.11.30
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Abstract

Recently, owing to global warming, average summer temperatures are increasing and the number of hot days is increasing is increasing, which leads to an increase in heat stroke. In particular, outdoor workers directly exposed to the heat are at higher risk of heat stroke; therefore, preventing heat-related illnesses and managing safety have become important. Although various wearable devices have been developed to prevent heat stroke for outdoor workers, applying various sensors to the safety helmets that workers must wear is an excellent alternative. In this study, we developed a smart helmet that measures various vital signs of the wearer such as body temperature, heart rate, and sweat rate; external environmental signals such as temperature and humidity; and movement signals of the wearer such as roll and pitch angles. The smart helmet can acquire the various data by connecting with a smartphone application. Environmental data can check the status of heat wave advisory, and the individual vital signs can monitor the health of workers. In addition, we developed an algorithm that classifies the risk of heat-related illness as normal and abnormal by inputting a set of vital signs of the wearer using a support vector machine technique, which is a machine learning technique that allows for rapid binary classification with high reliability. Furthermore, the classified results suggest that the safety manager can supervise the prevention of heat stroke by receiving feedback from the control system.

Keywords

Acknowledgement

본 논문은 행정안전부, 재난안전산업육성지원사업 (2019-MOIS32-028) 및 한국산업기술진흥원, 공공혁신수요기반 신기술사업화 (P0018415) 의 지원과 일부 한국기계연구원 기본사업 (NK238D)의 지원을 받아 수행된 연구임.

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