A Method for Estimating Temperature of Grasped Object using PPG Sensor

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Abstract

Wearable devices can obtain a variety of information, such as pulse waves, by measuring biometric data. They are applied to many services, including support for the elderly and medical assistance. In the measurement of pulse waves, capillaries dilate and constrict due to changes in skin temperature, which may result in changes in blood flow. Since changes in blood flow are the same as changes in pulse wave, it can be inferred that there is a correlation between skin temperature and pulse sensor readings, which changes depending on the temperature of the object being grasped. In this study, we propose a method for estimating the temperature of a grasped object using a photoplethysmography (PPG) sensor installed in a smartwatch or activity meter. By estimating the grasped object temperature, cold burns and temperature-related sensory disturbances such as unconsciously holding a hot object for a long time in people who have no pain sense due to congenital lack of pain perception or in the elderly who have no pain sense due to a functional decline in the cerebrum caused by dementia can be prevented. In this study, we propose a method to estimate the temperature of an object being touched using a PPG sensor. The proposed method measures the pulse wave from the PPG sensor and estimates the temperature from the rate of change of the difference between the peak of the pulse wave after touching the object and the pulse wave under normal conditions. The results of the temperature estimation experiment for liquids on four subjects showed that the proposed method could estimate the temperature with a 78.1% correct rate, and for solids on two subjects the rate was 75%.

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APA

Hotta, J., & Murao, K. (2023). A Method for Estimating Temperature of Grasped Object using PPG Sensor. In UbiComp/ISWC 2022 Adjunct - Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2022 ACM International Symposium on Wearable Computers (pp. 335–339). Association for Computing Machinery, Inc. https://doi.org/10.1145/3544793.3560396

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