Optimization of facial skin temperature-based anomaly detection model considering diurnal variation

• Published in: Artificial life and robotics Vol. 28; no. 2; pp. 394 - 402
• Main Authors: Takano, Masahito, Iwashita, Yuki, Nagumo, Kent, Oiwa, Kosuke, Nozawa, Akio
• Format: Journal Article
• Language: English
• Published: Tokyo Springer Japan 01.05.2023; Springer Nature B.V
• Subjects: Anomalies; Artificial Intelligence; Autonomic nervous system; Computation by Abstract Devices; Computer Science; Control; Diurnal variations; Mechatronics; Optimization; Original Article; Robotics; Skin; Skin temperature; Temperature; Thermal imaging; Infrared thermography; Deep learning; Variational autoencoder; Blood pressure; Remote health monitoring; Thermal face image
• ISSN: 1433-5298; 1614-7456
• DOI: 10.1007/s10015-023-00853-3

The amount of blood under the surface of skin is controlled by the autonomic nervous system and directly influences the facial skin temperature. Classification models have been used to estimate various physiological and psychological states of the human body using facial skin temperature. The anomaly detection method is required to monitor the facial skin temperature because of the difficulty in collecting anomalous samples. The normal state of the facial skin temperature fluctuates; hence, diurnal variation should be considered when applying anomaly detection methods to monitor the facial skin temperature. In a previous study, the anomaly detection method was applied to the facial skin temperature considering diurnal variation, and the normal and anomaly states were measured 16 times at 1-h intervals. A variational autoencoder (VAE) was applied to the normal-state data to construct an anomaly detection model. However, in many cases, anomalous states were not detected. The mean AUC (area under the receiver-operating characteristic curve) for the 16 experiments was 0.57 using the model of the previous study. The application of thermal images and VAE training is yet to be comprehensively studied. In this study, we improved anomaly detection accuracy for the facial skin temperature with diurnal variation by optimizing the method of thermal images and model structure. The mean AUC of the proposed model for the 16 experiments was 0.96.