On the Forecasting of Body Temperature using IoT and Machine Learning Techniques

Makhlouf, Khadidja; Hmidi, Zohra; Kahloul, Laïd; Benhrazallah, Saber; Ababsa, Tarek

doi:10.1109/ictaacs53298.2021.9715211

Cited by 5 publications

(1 citation statement)

References 17 publications

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“…Different ML models were trained and tested, and the results showed that the random forest model performed the best. The investigation in [ 43 ] presents an IoT system to enhance healthcare services and help professionals reduce their workload. The data were analyzed using ML techniques to detect adverse health conditions early.…”

Section: Related Workmentioning

confidence: 99%

A Low-Cost Wearable Device to Estimate Body Temperature Based on Wrist Temperature

Mata-Romero,

Simental-Martínez,

Guerrero-Osuna

et al. 2024

Sensors

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The remote monitoring of vital signs and healthcare provision has become an urgent necessity due to the impact of the COVID-19 pandemic on the world. Blood oxygen level, heart rate, and body temperature data are crucial for managing the disease and ensuring timely medical care. This study proposes a low-cost wearable device employing non-contact sensors to monitor, process, and visualize critical variables, focusing on body temperature measurement as a key health indicator. The wearable device developed offers a non-invasive and continuous method to gather wrist and forehead temperature data. However, since there is a discrepancy between wrist and actual forehead temperature, this study incorporates statistical methods and machine learning to estimate the core forehead temperature from the wrist. This research collects 2130 samples from 30 volunteers, and both the statistical least squares method and machine learning via linear regression are applied to analyze these data. It is observed that all models achieve a significant fit, but the third-degree polynomial model stands out in both approaches. It achieves an R2 value of 0.9769 in the statistical analysis and 0.9791 in machine learning.

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Section: Related Workmentioning

confidence: 99%