TY - JOUR
T1 - TherMobile
T2 - Measuring Body Temperature Using a Mobile Device
AU - Jun, Sanghoon
AU - Lee, Kilho
AU - Lee, Jinkyu
N1 - Publisher Copyright:
© 2001-2012 IEEE.
PY - 2022/7/1
Y1 - 2022/7/1
N2 - The spread of COVID-19 issues high demand on measuring body temperature, which necessitates thermometers. To alleviate a burden to equip/carry thermometers, this paper develops a framework 'Ther- Mobile' that measures body temperature using a commercial-off-the-shelf smartphone that most people carry everywhere. Considering that most (if not all) smartphones have a temperature sensor on its battery, we utilize heat transfer from a body part that makes contact with the smartphone, to the smartphone battery. To this end, we collect a time series of the smartphone battery temperature for different pairs of the initial temperature of the smartphone battery and the temperature of a body part, and then classify them. To enable the data collection and classification to infer the temperature of the body part, we address important practical issues, including how to gather data for different target temperatures of a body part (although human body temperature is not controllable), and how to minimize a burden for individual users to gather all necessary data. Our experiments demonstrate that 'Ther- Mobile' achieves 90.0% accuracy of measuring body temperature with 1.0°C granularity, enabling a commercial-off-the-shelf smartphone to substitute for a thermometer without any additional hardware.
AB - The spread of COVID-19 issues high demand on measuring body temperature, which necessitates thermometers. To alleviate a burden to equip/carry thermometers, this paper develops a framework 'Ther- Mobile' that measures body temperature using a commercial-off-the-shelf smartphone that most people carry everywhere. Considering that most (if not all) smartphones have a temperature sensor on its battery, we utilize heat transfer from a body part that makes contact with the smartphone, to the smartphone battery. To this end, we collect a time series of the smartphone battery temperature for different pairs of the initial temperature of the smartphone battery and the temperature of a body part, and then classify them. To enable the data collection and classification to infer the temperature of the body part, we address important practical issues, including how to gather data for different target temperatures of a body part (although human body temperature is not controllable), and how to minimize a burden for individual users to gather all necessary data. Our experiments demonstrate that 'Ther- Mobile' achieves 90.0% accuracy of measuring body temperature with 1.0°C granularity, enabling a commercial-off-the-shelf smartphone to substitute for a thermometer without any additional hardware.
KW - battery temperature sensor
KW - heat transfer
KW - Measuring body temperature
KW - smartphone
KW - support vector machine
UR - https://www.scopus.com/pages/publications/85131801310
U2 - 10.1109/JSEN.2022.3179726
DO - 10.1109/JSEN.2022.3179726
M3 - Article
AN - SCOPUS:85131801310
SN - 1530-437X
VL - 22
SP - 13338
EP - 13345
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 13
ER -