TY - GEN
T1 - An Embedded Non-Contact Body Temperature Measurement System with Automatic Face Tracking and Neural Network Regression
AU - Huang, Po Wei
AU - Chang, Tzu Hsuan
AU - Lee, Meng Ju
AU - Lin, Tzu Min
AU - Chung, Meng Liang
AU - Wu, Bing Fei
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/7/10
Y1 - 2017/7/10
N2 - 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.
AB - 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.
KW - Automatic Face-Tracking
KW - Fuzzy-control
KW - Neural Network Regression
KW - Temperature Estimation
UR - http://www.scopus.com/inward/record.url?scp=85027552961&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85027552961&partnerID=8YFLogxK
U2 - 10.1109/CACS.2016.7973902
DO - 10.1109/CACS.2016.7973902
M3 - Conference contribution
AN - SCOPUS:85027552961
T3 - 2016 International Automatic Control Conference, CACS 2016
SP - 161
EP - 166
BT - 2016 International Automatic Control Conference, CACS 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 International Automatic Control Conference, CACS 2016
Y2 - 9 November 2016 through 11 November 2016
ER -
