Method of Estimating Heatstroke Risk Using Wristwatch-type Device

Abstract

Keywords: heatstroke, WBGT, black globe temperature, neural network, wearable device As a method of estimating the risk of heatstroke with a wearable device, we have developed a method of calculating the wet bulb globe temperature (WBGT) by estimating the black globe temperature (T g ) only from sensors that can be mounted on a wristwatch-type device. In WBGT measurement, the conventional method requires a large sensor for measuring T g , and it has been difficult to grasp an individual's heatstroke risk. In this research, we proposed a method of estimating T g using a neural network and compared the estimation accuracy for different numbers of layers and nodes. In the T g range of 31 to 41 ℃, it was confirmed that when T g was estimated by the fully connected neural network of three layers and 20 nodes, the regression coefficient between the measured T g and the estimated T g was 0.90, indicating a high accuracy.

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

“…The Ministry of the Environment in Japan operates a web-based system [ 10 ] for viewing WBGT data for each region. In another example, Kaiho et al [ 11 ] attempted to calculate the WBGT using only the black-bulb temperature measured with a portable sensor, as opposed to the WBGT calculation that requires black-bulb temperature, dry-bulb temperature, and wet-bulb temperature. In addition, McCann et al [ 12 ] reported that WBGT can improve the performance of long-distance runners based on the effectiveness of the heat stroke guidelines of the National Collegiate Athletic Association.…”