Abstract
Measuring core body temperature has significant implications in various fields, including healthcare, sports medicine, and military operations. Existing core body temperature measurement, such as rectal and esophageal measurements, are invasive, costly, and may not provide continuous monitoring, highlighting the need for non-invasive and reliable alternatives. With the development of wearable and computer technology, predicting core body temperature based on continuous measurement of skin temperature, ambient temperature, and other physiological parameters has become a trend for future development. In this study, we proposed a novel non-invasive method for predicting core body temperature using wearable sensors. A stacking fusion model was proposed to improve the core temperature prediction accuracy. The skin temperature and ambient temperature were collected by a wearable device and processed to remove outliers. Simultaneously, the core body temperature collected by a capsule temperature sensor was used as the gold standard. Then, we used the preprocessed data to train a machine learning algorithm, which was validated on collecting human data. The results showed that the algorithm based on stacking model fusion performed best, with an RMSE of 0.0448, MAE of 0.0214, and MAPE of 0.0582%, indicating high precision in predicting core body temperature, which can be used for continuous monitoring and accurate diagnosis, effective medical treatment.
Original language | English |
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Title of host publication | 2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress |
Subtitle of host publication | DASC/PiCom/CBDCom/CyberSciTech 2023 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 499-504 |
Number of pages | 6 |
ISBN (Electronic) | 9798350304602 |
DOIs | |
Publication status | Published - 25 Dec 2023 |
Event | 2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023 - Abu Dhabi, United Arab Emirates Duration: 14 Nov 2023 → 17 Nov 2023 |
Conference
Conference | 2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023 |
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Country/Territory | United Arab Emirates |
City | Abu Dhabi |
Period | 14/11/23 → 17/11/23 |
Bibliographical note
Funding Information:ACKNOWLEDGEMENTS This study was sponsored by the Strategic Priority CAS Project under grant number XDB38010100; National Natural Science Foundation of China under grant number 62073310; the basic research project of Guangdong Province under grant number 2021A1515011838; Key R&D Plan of Guangdong Province under grant number 2022B1515120062; Joint Fund of NSFC and Chongqing under grant number U21A20447; and Guangdong Provincial Science and Technology Plan under grant number 2022A1515011557; Key project of NSFC with grant number 92057206; Shenzhen International Cooperation Project under grant number GJHZ20220913142808016; and Shenzhen Science and Technology Program under grant number JCYJ20190807161805817.
Keywords
- core body temperature
- machine learning
- prediction
- stacking model fusion
- wearable device