標題: Neural Network Based Luminance Variation Resistant Remote-Photoplethysmography for Driver's Heart Rate Monitoring
作者: Wu, Bing-Fei
Chu, Yun-Wei
Huang, Po-Wei
Chung, Meng-Liang
電機工程學系
電控工程研究所
Department of Electrical and Computer Engineering
Institute of Electrical and Control Engineering
關鍵字: Advanced driver assistance systems;artificial neural network;heart rate monitoring;health and safety;remote photoplethysmography;vehicle safety
公開日期: 1-Jan-2019
摘要: 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).
URI: http://dx.doi.org/10.1109/ACCESS.2019.2913664
http://hdl.handle.net/11536/152422
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2019.2913664
期刊: IEEE ACCESS
Volume: 7
起始頁: 57210
結束頁: 57225
Appears in Collections:Articles