산업 종사자 안전 강화를 위한 다기능 안전 헬멧 기반 중앙감시 플랫폼 프로토타입 개발
Development of a Multi-Functional Safety Helmet-based Central Monitoring Platform for the Safety Improvement of Industrial Workers: A Preliminary Study
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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 °C, 30.67 ± 0.40 °C, 31.35 ± 0.33 °C, 34.01 ± 0.23 °C, 35.27 ± 0.16 °C, 36.12 ± 0.30 °C, 39.43 ± 0.23 °C and 41.74 ± 0.35 °C when the measurements of the reference temperature sensor were 30, 32, 34, 36, 38, 40, 42 and 44 °C, 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.
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 °C, 30.67 ± 0.40 °C, 31.35 ± 0.33 °C, 34.01 ± 0.23 °C, 35.27 ± 0.16 °C, 36.12 ± 0.30 °C, 39.43 ± 0.23 °C and 41.74 ± 0.35 °C when the measurements of the reference temperature sensor were 30, 32, 34, 36, 38, 40, 42 and 44 °C, 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.
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