Browsing by Author "Chu, Yun-Wei"

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A Motion Robust Remote-PPG Approach to Driver's Health State Monitoring
(2017-01-01)
Wu, Bing-Fei
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Chu, Yun-Wei
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Huang, Po-Wei
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Chung, Meng-Liang
;
Lin, Tzu-Min
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.
Neural Network Based Luminance Variation Resistant Remote-Photoplethysmography for Driver's Heart Rate Monitoring
(2019-01-01)
Wu, Bing-Fei
;
Chu, Yun-Wei
;
Huang, Po-Wei
;
Chung, Meng-Liang
;
電機工程學系
The driver's physiological status has enormous value to public traffic safety and cannot be ignored nowadays. In addition, heart rate (HR) is one of the most important indicators of human's health status. When detecting the driver's HR, using traditional contact-type devices might bring about the driver's distraction or discomfort. On the contrary, the remote photoplethysmography (rPPG) technique is a better way to monitor a driver's HR in vehicle applications simply by using a web-camera without interfering the driver. Most of the rPPG studies intended to reduce the interference caused by facial motion or luminance changes in the indoor or controlled scenario, but there are relatively fewer discussions on outdoor scenarios. Consequently, the purpose of this paper is to enhance the rPPG technique to make it suitable for the outdoor driving scenarios and for monitoring the driver's HR in different weather conditions, including daytime and nighttime. We first utilize artificial neural network (ANN) and train multiple personalized ANN models for each driver. For predicting the drivers' HR beat more precisely, we propose the approach, adaptive neural network model selection (ANNMS), which adaptively selects a personalized ANN model based on different noise conditions. Our algorithm eliminates the effect of noises caused by the variations of facial luminance in eight outdoor driving scenarios. The proposed driver's HR beat monitoring system has been evaluated against the state-of-the-art rPPG techniques that are Chrominance signal-based (CHRO) and k-nearest neighbors-based (kNN) algorithms. Compared with the CHRO and kNN algorithms, the ANNMS reduces the mean absolute error from 14.71 bpm (CHRO) and 9.91 (kNN) to 4.51 bpm (ANNMS) and enhances the successrate-10, the probability in which the absolute error is below 10 bpm, from 44.1% (CHRO) and 56.3% (kNN) to 91.5% (ANNMS).