TY - GEN
T1 - A motion robust remote-PPG approach to driver’s health state monitoring
AU - Wu, Bing Fei
AU - Chu, Yun Wei
AU - Huang, Po Wei
AU - Chung, Meng Liang
AU - Lin, Tzu Min
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - With the surging significance of personal health care, driver’s physiological state is no longer negligible nowadays. Among all the indicators of health state in human, heart rate (HR) is one of the most cardinal indicators. The commonly used HR measurement is contact-type, might result in driver’s distraction and discomfort in the vehicle applications. To cope with this problem, remote photoplethysmography (rPPG) is utilized to monitor HR at a distance via a web camera. Nevertheless, the rPPG is not without its flaw. The main concern of the rPPG technique is the potential not-robustness result from the arbitrary motion. Consequently, the contribution of this paper is to conquer the motion noise when the car is driving and the driver’s health state is well monitored to enhance the public safety. The proposed algorithm is investigated in not only the indoor environment but as well the outdoor driving, which contains much more unpredictable motion. With k-nearest neighbor (kNN) classifier on chrominance-based features, the mean square error can be reduced from 30.6 to 2.79 bpm, approaching the medical instrument level. The proposed method can be applied to human improving driving safety for Advanced Driver Assistance Systems.
AB - With the surging significance of personal health care, driver’s physiological state is no longer negligible nowadays. Among all the indicators of health state in human, heart rate (HR) is one of the most cardinal indicators. The commonly used HR measurement is contact-type, might result in driver’s distraction and discomfort in the vehicle applications. To cope with this problem, remote photoplethysmography (rPPG) is utilized to monitor HR at a distance via a web camera. Nevertheless, the rPPG is not without its flaw. The main concern of the rPPG technique is the potential not-robustness result from the arbitrary motion. Consequently, the contribution of this paper is to conquer the motion noise when the car is driving and the driver’s health state is well monitored to enhance the public safety. The proposed algorithm is investigated in not only the indoor environment but as well the outdoor driving, which contains much more unpredictable motion. With k-nearest neighbor (kNN) classifier on chrominance-based features, the mean square error can be reduced from 30.6 to 2.79 bpm, approaching the medical instrument level. The proposed method can be applied to human improving driving safety for Advanced Driver Assistance Systems.
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U2 - 10.1007/978-3-319-54407-6_31
DO - 10.1007/978-3-319-54407-6_31
M3 - Conference contribution
AN - SCOPUS:85016219936
SN - 9783319544069
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 463
EP - 476
BT - Computer Vision - ACCV 2016 Workshops - ACCV 2016 International Workshops, Revised Selected Papers
A2 - Lu, Jiwen
A2 - Ma, Kai-Kuang
A2 - Chen, Chu-Song
PB - Springer Verlag
T2 - 13th Asian Conference on Computer Vision, ACCV 2016
Y2 - 20 November 2016 through 24 November 2016
ER -
