Literature DB >> 25375690

An innovative nonintrusive driver assistance system for vital signal monitoring.

Ye Sun, Xiong Bill Yu.   

Abstract

This paper describes an in-vehicle nonintrusive biopotential measurement system for driver health monitoring and fatigue detection. Previous research has found that the physiological signals including eye features, electrocardiography (ECG), electroencephalography (EEG) and their secondary parameters such as heart rate and HR variability are good indicators of health state as well as driver fatigue. A conventional biopotential measurement system requires the electrodes to be in contact with human body. This not only interferes with the driver operation, but also is not feasible for long-term monitoring purpose. The driver assistance system in this paper can remotely detect the biopotential signals with no physical contact with human skin. With delicate sensor and electronic design, ECG, EEG, and eye blinking can be measured. Experiments were conducted on a high fidelity driving simulator to validate the system performance. The system was found to be able to detect the ECG/EEG signals through cloth or hair with no contact with skin. Eye blinking activities can also be detected at a distance of 10 cm. Digital signal processing algorithms were developed to decimate the signal noise and extract the physiological features. The extracted features from the vital signals were further analyzed to assess the potential criterion for alertness and drowsiness determination.

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Year:  2014        PMID: 25375690     DOI: 10.1109/JBHI.2014.2305403

Source DB:  PubMed          Journal:  IEEE J Biomed Health Inform        ISSN: 2168-2194            Impact factor:   5.772


  17 in total

1.  Automatic Driver Drowsiness Detection Using Artificial Neural Network Based on Visual Facial Descriptors: Pilot Study.

Authors:  Papangkorn Inkeaw; Pimwarat Srikummoon; Jeerayut Chaijaruwanich; Patrinee Traisathit; Suphakit Awiphan; Juthamas Inchai; Ratirat Worasuthaneewan; Theerakorn Theerakittikul
Journal:  Nat Sci Sleep       Date:  2022-09-14

2.  A Precise Drunk Driving Detection Using Weighted Kernel Based on Electrocardiogram.

Authors:  Chung Kit Wu; Kim Fung Tsang; Hao Ran Chi; Faan Hei Hung
Journal:  Sensors (Basel)       Date:  2016-05-09       Impact factor: 3.576

Review 3.  Integration of Body Sensor Networks and Vehicular Ad-hoc Networks for Traffic Safety.

Authors:  Angelica Reyes-Muñoz; Mari Carmen Domingo; Marco Antonio López-Trinidad; José Luis Delgado
Journal:  Sensors (Basel)       Date:  2016-01-15       Impact factor: 3.576

4.  A Context-Aware S-Health Service System for Drivers.

Authors:  Jingkun Chang; Wenbin Yao; Xiaoyong Li
Journal:  Sensors (Basel)       Date:  2017-03-17       Impact factor: 3.576

5.  Design of an Adaptive Human-Machine System Based on Dynamical Pattern Recognition of Cognitive Task-Load.

Authors:  Jianhua Zhang; Zhong Yin; Rubin Wang
Journal:  Front Neurosci       Date:  2017-03-17       Impact factor: 4.677

6.  Unobtrusive Vital Sign Monitoring in Automotive Environments-A Review.

Authors:  Steffen Leonhardt; Lennart Leicht; Daniel Teichmann
Journal:  Sensors (Basel)       Date:  2018-09-13       Impact factor: 3.576

7.  Ultra-short term HRV features as surrogates of short term HRV: a case study on mental stress detection in real life.

Authors:  R Castaldo; L Montesinos; P Melillo; C James; L Pecchia
Journal:  BMC Med Inform Decis Mak       Date:  2019-01-17       Impact factor: 2.796

Review 8.  REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health.

Authors:  Maryam Pishgar; Salah Fuad Issa; Margaret Sietsema; Preethi Pratap; Houshang Darabi
Journal:  Int J Environ Res Public Health       Date:  2021-06-22       Impact factor: 3.390

9.  Reduction of Motion Artifacts and Improvement of R Peak Detecting Accuracy Using Adjacent Non-Intrusive ECG Sensors.

Authors:  Minho Choi; Jae Jin Jeong; Seung Hun Kim; Sang Woo Kim
Journal:  Sensors (Basel)       Date:  2016-05-17       Impact factor: 3.576

10.  A Generic Design of Driver Drowsiness and Stress Recognition Using MOGA Optimized Deep MKL-SVM.

Authors:  Kwok Tai Chui; Miltiadis D Lytras; Ryan Wen Liu
Journal:  Sensors (Basel)       Date:  2020-03-07       Impact factor: 3.576

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