IoT-enabled Edge Impulse approach for heat stress prediction in outdoor settings

Abstract

Several international organizations of public health or countries have predicted the rise of heat-related illness cases due to climate change, which result high environment temperature. Previous studies of heat-related illness prediction using internet of things (IoT) and machine learning (ML) are mainly focusing on early detection or prediction of heat stroke incidence. To overcome the problem of heat stress prediction in outdoor settings, especially for an individual, the objective of this study is to identify a prediction method for heat stress using IoT technology and analyze the accuracy of the identified prediction model. Arduino nano 33 BLE sense with Bluetooth low energy (BLE) connectivity, HTS221 embedded environment temperature and humidity sensor, MLX90614 skin temperature sensor, and MAX30100 heart rate sensor were used to build IoT based wearable device. Besides, Python language is used for data pre-processing and data labelling after getting the sensor data from wearable device. Lastly, model training using neural network algorithms was directed in Edge Impulse. The result shows 94.6% of training accuracy with the loss of 0.27 and 90.22% of accuracy in testing set.