scholarly journals Video Analytic Based Health Monitoring for Driver in Moving Vehicle by Extracting Effective Heart Rate Inducing Features

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Kanghyu Lee ◽  
David K. Han ◽  
Hanseok Ko
Keyword(s):  
Heart Rate ◽  
Estimation Method ◽  
Region Of Interest ◽  
Ensemble Empirical Mode Decomposition ◽  
Superior Performance ◽  
Indoor Environments ◽  
Vehicle Interior ◽  
Moving Vehicle ◽  
Ambient Lighting ◽  
Driving Conditions

We propose a novel remote heart rate (HR) estimation method using facial images based on video analytics. Most of previous methods have been demonstrated in well-controlled indoor environments. In contrast, this paper proposes a practical video analytic framework under actual driving conditions by extracting key HR inducing features. In particular, when cars are driven, effective and stable HR estimation becomes challenging as there are many dynamic elements, such as rapid illumination changes, vibrations, and ambient lighting that can exist in the vehicle interior. To overcome those disturbances of HR estimation, the driver face region is first detected and cropped to the region of interest (RoI). Second, the components related to HR are extracted from mixed noisy components using ensemble empirical mode decomposition (EEMD). Finally, the extracted signal is analyzed in frequency domain and smoothed with temporal filtering. To verify our approach, the proposed method is compared with recent prominent methods employing a public HCI dataset. It has been demonstrated that the proposed approach delivers superior performance under driving conditions using Bland-Altman plots.

scholarly journals Video-Based Contactless Heart-Rate Detection and Counting via Joint Blind Source Separation with Adaptive Noise Canceller

2019 ◽  
Vol 9 (20) ◽  
pp. 4349 ◽  
Author(s):  
Kanghyu Lee ◽  
Junmuk Lee ◽  
Changwoo Ha ◽  
Minseok Han ◽  
Hanseok Ko
Keyword(s):  
Heart Rate ◽  
Blind Source Separation ◽  
Source Separation ◽  
Ensemble Empirical Mode Decomposition ◽  
Human Motion ◽  
Indoor Environments ◽  
State Monitoring ◽  
Driver Assistance Systems ◽  
Mode Decomposition ◽  
Adaptive Noise Canceller

Driver assistance systems are a major focus of the automotive industry. Although technological functions that help drivers are improving, the monitoring of driver state functions receives less attention. In this respect, the human heart rate (HR) is one of the most important bio-signals, and it can be detected remotely using consumer-grade cameras. Based on this, a video-based driver state monitoring system using HR signals is proposed in this paper. In a practical automotive environment, monitoring the HR is very challenging due to changes in illumination, vibrations, and human motion. In order to overcome these problems, source separation strategies were employed using joint blind source separation, and feature combination was adopted to maximize HR variation. Noise-assisted data analysis was then adopted using ensemble empirical mode decomposition to extract the pure HR. Finally, power spectral density analysis was conducted in the frequency domain, and a post-processing smoothing filter was applied. The performance of the proposed approach was tested based on commonly employed metrics using the MAHNOB-HCI public dataset and compared with recently proposed competing methods. The experimental results proved that our method is robust for a variety of driving conditions based on testing using a driving dataset and static indoor environments.

Effects of Region of Interest Size on Heart Rate Assessment through Video Magnification

2021 ◽  
Author(s):  
Leen Yassin Kassab ◽  
Andrew Law ◽  
Bruce Wallace ◽  
Julien Lariviere-Chartier ◽  
Rafik Goubran ◽  
...  
Keyword(s):  
Heart Rate ◽  
Region Of Interest

scholarly journals Heart Rate Variability for Classification of Alert Versus Sleep Deprived Drivers in Real Road Driving Conditions

2020 ◽  
pp. 1-10 ◽  
Author(s):  
Anna Persson ◽  
Hanna Jonasson ◽  
Ingemar Fredriksson ◽  
Urban Wiklund ◽  
Christer Ahlstrom
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Driving Conditions

scholarly journals Robust Visual Compass Using Hybrid Features for Indoor Environments

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 220 ◽  
Author(s):  
Ruibin Guo ◽  
Keju Peng ◽  
Dongxiang Zhou ◽  
Yunhui Liu
Keyword(s):  
Autonomous Navigation ◽  
Estimation Method ◽  
Rotation Matrix ◽  
Indoor Environments ◽  
3D Mapping ◽  
Orientation Estimation ◽  
Hybrid Features ◽  
Depth Images ◽  
Improved Accuracy ◽  
Crucial Part

Orientation estimation is a crucial part of robotics tasks such as motion control, autonomous navigation, and 3D mapping. In this paper, we propose a robust visual-based method to estimate robots’ drift-free orientation with RGB-D cameras. First, we detect and track hybrid features (i.e., plane, line, and point) from color and depth images, which provides reliable constraints even in uncharacteristic environments with low texture or no consistent lines. Then, we construct a cost function based on these features and, by minimizing this function, we obtain the accurate rotation matrix of each captured frame with respect to its reference keyframe. Furthermore, we present a vanishing direction-estimation method to extract the Manhattan World (MW) axes; by aligning the current MW axes with the global MW axes, we refine the aforementioned rotation matrix of each keyframe and achieve drift-free orientation. Experiments on public RGB-D datasets demonstrate the robustness and accuracy of the proposed algorithm for orientation estimation. In addition, we have applied our proposed visual compass to pose estimation, and the evaluation on public sequences shows improved accuracy.

scholarly journals A Novel Real-Time Human Heart Rate Estimation Method for Noncontact Vital Sign Radar Detection

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 88689-88699
Author(s):  
Yipeng Ding ◽  
Xiali Yu ◽  
Chengxi Lei ◽  
Yinhua Sun ◽  
Xuemei Xu ◽  
...  
Keyword(s):  
Heart Rate ◽  
Real Time ◽  
Vital Sign ◽  
Human Heart ◽  
Estimation Method ◽  
Radar Detection ◽  
Rate Estimation ◽  
Heart Rate Estimation

Algorithm of Moving Vehicle Detection by Improved Gaussian Mixture Model Based on ROI

2015 ◽  
Vol 734 ◽  
pp. 463-467 ◽  
Author(s):  
Pan Pan Zhang ◽  
Chun Yang Mu ◽  
Xing Ma ◽  
Fu Lu Xu
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Region Of Interest ◽  
Gaussian Mixture ◽  
Background Model ◽  
Morphological Filter ◽  
Improved Method ◽  
Moving Vehicle ◽  
Model Based ◽  
Moving Vehicle Detection

Detection of moving object is a hot topic in computer vision. Traditionally, it is detected for every pixel in whole image by Gaussian mixture background model, which may waste more time and space. In order to improving the computational efficiency, an advanced Gaussian mixture model based on Region of Interest was proposed. Firstly, the solution finds out the most probably region where the target may turn up. And then Gaussian mixture background model is built in this area. Finally, morphological filter algorithm is used for improving integrity of the detected targets. Results show that the improved method could have a more perfect detection but no more time increasing than typical method.

The Borg Scale Validation for a Consumer Product - Automobile Polishes

1984 ◽  
Vol 28 (1) ◽  
pp. 98-101
Author(s):  
J. Kreifeldt ◽  
P. Hill ◽  
M. M. Clarke ◽  
J. Draper
Keyword(s):  
Heart Rate ◽  
Scale Validation ◽  
Bicycle Ergometer ◽  
Weight Lifting ◽  
Superior Performance ◽  
Work Effort ◽  
Borg Scale ◽  
Predictive Utility ◽  
Perceived Effort ◽  
Numerical Rating

The Borg scale which is purported to correlate subjectively perceived heart rate with objective physiological workload was tested for its predictive utility by subjects using 5 different car wax formulations. Subjects applied and removed the waxes from a high gloss painted panel. The amount of effort and time expended with each wax were determined using a force platform and timing device. Subjects also gave a Borg scale numerical rating as they used each wax. The reported high correlation between Borg scale ratings and exercises using a bicycle ergometer, weight lifting, etc. suggested that it might be a reliable predictor of workload effort or time in such common tasks as car waxing. Results indicate that the ability of the Borg values to predict either amount of time taken or effort expended was moderate (r =.58) on average, with a relatively high correlation between time and effort (r = .61). The rank orders of averages for Borg values and work effort, however, agreed well across the five waxes. Additionally, results clearly indicated superior performance (actual and perceived effort) of a newly formulated wax.

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