An attempt to construct the individual model of daily facial skin temperature using variational autoencoder

• Published in: Artificial Life and Robotics Vol. 26; no. 4; pp. 488 - 493
• Main Authors: Masaki, Ayaka, Nagumo, Kent, Iwashita, Yuki, Oiwa, Kosuke, Nozawa, Akio
• Format: Journal Article
• Language: English
• Published: Tokyo Springer Science and Business Media LLC 01.11.2021; Springer Japan; Springer Nature B.V
• Subjects: Anomalies; Artificial Intelligence; Computation by Abstract Devices; Computer Science; Control; Diurnal variations; Mechatronics; Original; Original Article; Robotics; Skin temperature; Infrared thermography; Deep learning; Variational autoencoder; Hotelling’s theory; Facial skin temperature; Anomaly detection
• ISSN: 1433-5298; 1614-7456
• DOI: 10.1007/s10015-021-00699-7

Facial skin temperature (FST) has also gained prominence as an indicator for detecting anomalies such as fever due to the COVID-19. When FST is used for engineering applications, it is enough to be able to recognize normal. We are also focusing on research to detect some anomaly in FST. In a previous study, it was confirmed that abnormal and normal conditions could be separated based on FST by using a variational autoencoder (VAE), a deep generative model. However, the simulations so far have been a far cry from reality. In this study, normal FST with a diurnal variation component was defined as a normal state, and a model of normal FST in daily life was individually reconstructed using VAE. Using the constructed model, the anomaly detection performance was evaluated by applying the Hotelling theory. As a result, the area under the curve (AUC) value in ROC analysis was confirmed to be 0.89 to 1.00 in two subjects.