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.
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