Research Article
Received WiFi Signal Strength Monitoring for Contactless Body Temperature Classification
@INPROCEEDINGS{10.1007/978-3-030-95593-9_10, author={Vincent Ha and Ani Nahapetian}, title={Received WiFi Signal Strength Monitoring for Contactless Body Temperature Classification}, proceedings={Body Area Networks. Smart IoT and Big Data for Intelligent Health Management. 16th EAI International Conference, BODYNETS 2021, Virtual Event, October 25-26, 2021, Proceedings}, proceedings_a={BODYNETS}, year={2022}, month={2}, keywords={Body temperature WiFi Smartphones}, doi={10.1007/978-3-030-95593-9_10} }- Vincent Ha
Ani Nahapetian
Year: 2022
Received WiFi Signal Strength Monitoring for Contactless Body Temperature Classification
BODYNETS
Springer
DOI: 10.1007/978-3-030-95593-9_10
Abstract
Currently, non-contact body temperature monitoring requires specialized thermometers, such as non-contact infrared thermometers (NCIT), to achieve a reading. This work explores an alternative way of classifying temperature using the ubiquitous WiFi waveform. By merely observing the change in the received signal strength indicator (RSSI), body temperature can be classified as below normal, normal, or warm. Using a smartphone as the receiver and a router or another phone as the transmitter, experimental results show that temperature is inversely correlated with RSSI. The findings also indicate that WiFi RSSI is less variable when the temperature is cooler. Our classification can correctly identify the temperature class from a single RSSI reading 56.86% of the time. It can correctly identify a cool reading 61.11% of the time, a normal reading 58.82% of the time, and a warm reading 50% of the time.
