Citation

Lim, Ke Yin and Yogarayan, Sumendra and Abdul Razak, Siti Fatimah and Sayeed, Md. Shohel and Bukar, Umar Ali (2025) IoT-enabled Edge Impulse approach for heat stress prediction in outdoor settings. IAES International Journal of Artificial Intelligence (IJ-AI), 14 (5). p. 3934. ISSN 2089-4872

Text IoT-enabled Edge Impulse approach for heat stress prediction in outdoor settings _ Ke Yin _ IAES International Journal of Artificial Intelligence (IJ-AI).pdf - Published Version Restricted to Repository staff only Download (7MB)

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 analyzethe accuracy of the identified prediction model. Arduino nano 33 BLE sense with Bluetoothlow energy (BLE) connectivity, HTS221 embedded environment temperature and humidity sensor, MLX90614 skin temperature sensor,and MAX30100 heartrate 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

Item Type:	Article
Uncontrolled Keywords:	Internet of things, machine learning, prediction
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	22 Dec 2025 06:07
Last Modified:	22 Dec 2025 06:07
URII:	http://shdl.mmu.edu.my/id/eprint/15110

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