Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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Development of a Multi-Functional Safety Helmet-based Central Monitoring Platform for the Safety Improvement of Industrial Workers: A Preliminary Study

산업 종사자 안전 강화를 위한 다기능 안전 헬멧 기반 중앙감시 플랫폼 프로토타입 개발

  • Kim, Gun Ho (Department of Biomedical Engineering, School of Medicine, Pusan National University) ;
  • Lee, Hong Je (Department of Nuclear Medicine, Dongnam Institute of Radiological & Medical Sciences) ;
  • Yun, Sung Uk (Department of Biomedical Engineering, Pusan National University Yangsan Hospital) ;
  • Nam, Kyoung Won (Department of Biomedical Engineering, School of Medicine, Pusan National University)
  • 김건호 (부산대학교 의과대학 의공학교실) ;
  • 이홍제 (동남권원자력의학원 핵의학과) ;
  • 윤성욱 (양산부산대학교병원 의공학과) ;
  • 남경원 (부산대학교 의과대학 의공학교실)
  • Received : 2021.02.16
  • Accepted : 2021.04.07
  • Published : 2021.04.30
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Abstract

Purpose: In this study, we proposed a multi-functional safety helmet-based central monitoring platform to improve the safety of industrial workers. Materials and Methods: The manufactured prototype safety helmet contained sensors to detect heart rate, body temperature, wearing state, movement state and shock state. Implemented HTML-based central monitoring platform receives real-time measurements from the helmet via a wifi network, stores data into the SQL table, and displays real-time and historical data of the measurements using chrome web browser. Results: Experimental results showed that heart rate measurements of the helmet were 29.37 ± 0.49 bpm, 59.50 ± 0.51 bpm and 159.57 ± 1.41 bpm when the setting of the utilized ECG simulator was 30, 60 and 160 bpm. Temperature measurements of the helmet were 29.26 ± 0.43 ℃, 30.67 ± 0.40 ℃, 31.35 ± 0.33 ℃, 34.01 ± 0.23 ℃, 35.27 ± 0.16 ℃, 36.12 ± 0.30 ℃, 39.43 ± 0.23 ℃ and 41.74 ± 0.35 ℃ when the measurements of the reference temperature sensor were 30, 32, 34, 36, 38, 40, 42 and 44 ℃, respectively, and the linear regression [Y = AX + B; A = 0.873, B = 0.412, R2 = 0.972] was applied to the measurements to reduce sensor error. In addition, the implemented automatic black-out detection algorithm showed almost 100% accuracies during the experiments. Conclusion: Based on these experimental results, we expect that the proposed central monitoring platform showed the possibility to improve the safety of industrial workers, although more dedicated monitoring functions should be added to the current prototype system in future studies.

Keywords

Acknowledgement

본 논문은 한국수력원자력(주)에서 재원을 부담하여 부산대학교 및 양산부산대학교병원에서 공동으로 수행한 연구결과입니다(2018 K-CLOUD 사업. No. 안전-3).

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