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. 2022 Jan 5;22(1):395.
doi: 10.3390/s22010395.

Heat Stroke Prevention in Hot Specific Occupational Environment Enhanced by Supervised Machine Learning with Personalized Vital Signs

Takunori Shimazaki  1   2 , Daisuke Anzai  3 , Kenta Watanabe  4 , Atsushi Nakajima  4 , Mitsuhiro Fukuda  5 , Shingo Ata  2
Affiliations

Heat Stroke Prevention in Hot Specific Occupational Environment Enhanced by Supervised Machine Learning with Personalized Vital Signs

Takunori Shimazaki et al. Sensors (Basel). .

Abstract

Recently, wet-bulb globe temperature (WBGT) has attracted a lot of attention as a useful index for measuring heat strokes even when core body temperature cannot be available for the prevention. However, because the WBGT is only valid in the vicinity of the WBGT meter, the actual ambient heat could be different even in the same room owing to ventilation, clothes, and body size, especially in hot specific occupational environments. To realize reliable heat stroke prevention in hot working places, we proposed a new personalized vital sign index, which is combined with several types of vital data, including the personalized heat strain temperature (pHST) index based on the temperature/humidity measurement to adjust the WBGT at the individual level. In this study, a wearable device was equipped with the proposed pHST meter, a heart rate monitor, and an accelerometer. Additionally, supervised machine learning based on the proposed personalized vital index was introduced to improve the prevention accuracy. Our developed system with the proposed vital sign index achieved a prevention accuracy of 85.2% in a hot occupational experiment in the summer season, where the true positive rate and true negative rate were 96.3% and 83.7%, respectively.

Keywords: WBGT; heat stroke prevention; supervised machine learning; vital sensing.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Example of hot specific working environment (train maintenance factory).
Figure 2
Figure 2
Principle of personalized heat strain temperature (pHST) measurement.
Figure 3
Figure 3
Principle of motion artifacts cancellation.
Figure 4
Figure 4
Survey form in before- and after-workings.
Figure 5
Figure 5
Developed wearable device for vital data sensing.
Figure 6
Figure 6
System development to collect vital sensing data.
Figure 7
Figure 7
Variation of heart rate, pHST, environmental temperatures, and wind speed.
Figure 8
Figure 8
Performance evaluation for different classifiers.
Figure 9
Figure 9
Dependency of feature selection on prevention accuracy.
Figure 10
Figure 10
Confusion matrix for KNN classifier.
See this image and copyright information in PMC

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