An Embedded Non-Contact Body Temperature Measurement System with Automatic Face Tracking and Neural Network Regression

Abstract

In the last decade, many advances have been made in the field of automatic temperature estimation, including wearable sensor technologies (WST), infrared thermography (IRT), and non-contact infrared thermometer (NCIT). In contrast with the WST and IRT, NCIT is inexpensive without the risk of potential skin irritation. Nevertheless, NCIT is limited in short valid estimation distance (<;12 cm), resulting in the non-satisfaction of the surging application requirements nowadays. This paper proposed an algorithm based on Neural Network Regression not only to reduce the error from 0.6° to 0.12°, which is close to the medical instrument level, but as well to lengthen the valid distance to the range between 50 cm and 100 cm. Furthermore, this study developed an embedded automatic body temperature estimation system which could continuously and unconsciously measure the human temperature in real-time. Integrated with face tracking and fuzzy-control of Pan-tilt unit, the system ensures that human face is focused while measuring. With wireless communication techniques, users can review their physiological Information via App and Web, which is beneficial to remote healthcare.