Detecting Drivers’ Stress in Emergency Using Physiological Sensors

2015 ◽  
Vol 742 ◽  
pp. 150-157
Author(s):  
Ying Chen
Keyword(s):  
Heart Rate ◽  
Stress State ◽  
Driving Simulator ◽  
Time Frame ◽  
Training System ◽  
Heart Rate Variability Analysis ◽  
Continuous Measure ◽  
Physiological Sensors ◽  
Emergency Scenarios ◽  
Driver Stress

This paper presented methods for collecting and analyzing heart rate data during virtual-world driving task in emergency to distinguish and assess a driver’s stress state. The primer study had developed the driver stress training system loaded on driving simulator to display 14 typical emergency scenarios in different road environment. Electrocardiogram was recorded continuously by Physiological Sensors while drivers followed a random simulated driving on the driving simulator. Data from 30 drivers of at least 10 times per scenario were collected for analysis. The data were analyzed in two ways of HRV (Heart Rate Variability) analysis. Analysis I used time domain indexes of data during the driving in the whole training to distinguish the stress occur during the time frame of the scenario with an accuracy of over 98% across multiple drivers and driving days. Analysis II compared frequency indexes of data in stress state in emergency and in clam, with a metric of observable stressors created by the scenarios. The results showed that heart rate metrics is most closely correlated with driver stress level for most drivers studied. These findings confirmed that heart rate signals can provide a metric of driver stress in future cars capable of physiological monitoring. Such measurement could be used to help manage non-critical and critical in-vehicle information systems and could also provide a continuous measure of how different road and traffic conditions affect drivers. Physiological sensors can be used in driving assistance system in future.

scholarly journals Driver Stress State Evaluation by Means of Thermal Imaging: A Supervised Machine Learning Approach Based on ECG Signal

2020 ◽  
Vol 10 (16) ◽  
pp. 5673 ◽  
Author(s):  
Daniela Cardone ◽  
David Perpetuini ◽  
Chiara Filippini ◽  
Edoardo Spadolini ◽  
Lorenza Mancini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stress State ◽  
Thermal Imaging ◽  
Driving Simulator ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Thermal Features ◽  
Driver Stress

Traffic accidents determine a large number of injuries, sometimes fatal, every year. Among other factors affecting a driver’s performance, an important role is played by stress which can decrease decision-making capabilities and situational awareness. In this perspective, it would be beneficial to develop a non-invasive driver stress monitoring system able to recognize the driver’s altered state. In this study, a contactless procedure for drivers’ stress state assessment by means of thermal infrared imaging was investigated. Thermal imaging was acquired during an experiment on a driving simulator, and thermal features of stress were investigated with comparison to a gold-standard metric (i.e., the stress index, SI) extracted from contact electrocardiography (ECG). A data-driven multivariate machine learning approach based on a non-linear support vector regression (SVR) was employed to estimate the SI through thermal features extracted from facial regions of interest (i.e., nose tip, nostrils, glabella). The predicted SI showed a good correlation with the real SI (r = 0.61, p = ~0). A two-level classification of the stress state (STRESS, SI ≥ 150, versus NO STRESS, SI < 150) was then performed based on the predicted SI. The ROC analysis showed a good classification performance with an AUC of 0.80, a sensitivity of 77%, and a specificity of 78%.

scholarly journals Sensitivity of Physiological Measures of Acute Driver Stress: A Meta-Analytic Review

2021 ◽  
Vol 2 ◽  
Author(s):  
Laora Kerautret ◽  
Stephanie Dabic ◽  
Jordan Navarro
Keyword(s):  
Heart Rate ◽  
Stress Responses ◽  
Driving Simulator ◽  
High Stress ◽  
Individual Factors ◽  
Physiological Measures ◽  
Analytic Review ◽  
Major Interest ◽  
Experimental Apparatus ◽  
Driver Stress

Background: The link between driving performance impairment and driver stress is well-established. Identifying and understanding driver stress is therefore of major interest in terms of safety. Although many studies have examined various physiological measures to identify driver stress, none of these has as yet been definitively confirmed as offering definitive all-round validity in practice.Aims: Based on the data available in the literature, our main goal was to provide a quantitative assessment of the sensitivity of the physiological measures used to identify driver stress. The secondary goal was to assess the influence of individual factors (i.e., characteristics of the driver) and ambient factors (i.e., characteristics of the context) on driver stress. Age and gender were investigated as individual factors. Ambient factors were considered through the experimental apparatus (real-road vs. driving simulator), automation driving (manual driving vs. fully autonomous driving) and stressor exposure duration (short vs. long-term).Method: Nine meta-analyses were conducted to quantify the changes in each physiological measure during high-stress vs. low-stress driving. Meta-regressions and subgroup analyses were performed to assess the moderating effect of individual and ambient factors on driver stress.Results: Changes in stress responses suggest that several measures are sensitive to levels of driver stress, including heart rate, R-R intervals (RRI) and pupil diameter. No influence of individual and ambient factors was observed for heart rate.Applications and Perspective: These results provide an initial guide to researchers and practitioners when selecting physiological measures for quantifying driver stress. Based on the results, it is recommended that future research and practice use (i) multiple physiological measures, (ii) a triangulation-based methodology (combination of measurement modalities), and (iii) a multifactorial approach (analysis of the interaction of stressors and moderators).

scholarly journals Field Monitoring the Effects of Overnight Shift Work on Specialist Tactical Police Training with Heart Rate Variability Analysis

2021 ◽  
Vol 13 (14) ◽  
pp. 7895
Author(s):  
Colin Tomes ◽  
Ben Schram ◽  
Robin Orr
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Shift Work ◽  
Emergency Response ◽  
Police Training ◽  
Heart Rate Variability Analysis ◽  
Response Team ◽  
Hrv Analysis ◽  
And Performance ◽  
Personnel Allocation

Police work exposes officers to high levels of stress. Special emergency response team (SERT) service exposes personnel to additional demands. Specifically, the circadian cycles of SERT operators are subject to disruption, resulting in decreased capacity to compensate in response to changing demands. Adaptive regulation loss can be measured through heart rate variability (HRV) analysis. While HRV Trends with health and performance indicators, few studies have assessed the effect of overnight shift work on HRV in specialist police. Therefore, this study aimed to determine the effects overnight shift work on HRV in specialist police. HRV was analysed in 11 SERT officers and a significant (p = 0.037) difference was found in pRR50 levels across the training day (percentage of R-R intervals varying by >50 ms) between those who were off-duty and those who were on duty the night prior. HRV may be a valuable metric for quantifying load holistically and can be incorporated into health and fitness monitoring and personnel allocation decision making.

scholarly journals Heart rate variability analysis for the identification of the preictal interval in patients with drug-resistant epilepsy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Adriana Leal ◽  
Mauro F. Pinto ◽  
Fábio Lopes ◽  
Anna M. Bianchi ◽  
Jorge Henriques ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Linear Time ◽  
Epileptic Seizures ◽  
Heart Rate Variability Analysis ◽  
Drug Resistant ◽  
Clustering Methods ◽  
Temporal Lobe Seizures ◽  
New Perspective ◽  
Drug Resistant Epilepsy

AbstractElectrocardiogram (ECG) recordings, lasting hours before epileptic seizures, have been studied in the search for evidence of the existence of a preictal interval that follows a normal ECG trace and precedes the seizure’s clinical manifestation. The preictal interval has not yet been clinically parametrized. Furthermore, the duration of this interval varies for seizures both among patients and from the same patient. In this study, we performed a heart rate variability (HRV) analysis to investigate the discriminative power of the features of HRV in the identification of the preictal interval. HRV information extracted from the linear time and frequency domains as well as from nonlinear dynamics were analysed. We inspected data from 238 temporal lobe seizures recorded from 41 patients with drug-resistant epilepsy from the EPILEPSIAE database. Unsupervised methods were applied to the HRV feature dataset, thus leading to a new perspective in preictal interval characterization. Distinguishable preictal behaviour was exhibited by 41% of the seizures and 90% of the patients. Half of the preictal intervals were identified in the 40 min before seizure onset. The results demonstrate the potential of applying clustering methods to HRV features to deepen the current understanding of the preictal state.

scholarly journals Autoencoder-Based Extrasystole Detection and Modification of RRI Data for Precise Heart Rate Variability Analysis

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3235
Author(s):  
Koichi Fujiwara ◽  
Shota Miyatani ◽  
Asuka Goda ◽  
Miho Miyajima ◽  
Tetsuo Sasano ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Mean Squared Error ◽  
False Positive Rate ◽  
Premature Ventricular Contraction ◽  
Heart Rate Variability Analysis ◽  
Unified Framework ◽  
Atrial Contraction ◽  
Premature Atrial Contraction ◽  
Positive Rate

Heart rate variability, which is the fluctuation of the R-R interval (RRI) in electrocardiograms (ECG), has been widely adopted for autonomous evaluation. Since the HRV features that are extracted from RRI data easily fluctuate when arrhythmia occurs, RRI data with arrhythmia need to be modified appropriately before HRV analysis. In this study, we consider two types of extrasystoles—premature ventricular contraction (PVC) and premature atrial contraction (PAC)—which are types of extrasystoles that occur every day, even in healthy persons who have no cardiovascular diseases. A unified framework for ectopic RRI detection and a modification algorithm that utilizes an autoencoder (AE) type of neural network is proposed. The proposed framework consists of extrasystole occurrence detection from the RRI data and modification, whose targets are PVC and PAC. The RRI data are monitored by means of the AE in real time in the detection phase, and a denoising autoencoder (DAE) modifies the ectopic RRI caused by the detected extrasystole. These are referred to as AE-based extrasystole detection (AED) and DAE-based extrasystole modification (DAEM), respectively. The proposed framework was applied to real RRI data with PVC and PAC. The result showed that AED achieved a sensitivity of 93% and a false positive rate of 0.08 times per hour. The root mean squared error of the modified RRI decreased to 31% in PVC and 73% in PAC from the original RRI data by DAEM. In addition, the proposed framework was validated through application to a clinical epileptic seizure problem, which showed that it correctly suppressed the false positives caused by PVC. Thus, the proposed framework can contribute to realizing accurate HRV-based health monitoring and medical sensing systems.

scholarly journals Heart Rate Variability Analyses in Parkinson’s Disease: A Systematic Review and Meta-Analysis

2021 ◽  
Vol 11 (8) ◽  
pp. 959
Author(s):  
Konstantin G. Heimrich ◽  
Thomas Lehmann ◽  
Peter Schlattmann ◽  
Tino Prell
Keyword(s):  
Parkinson’S Disease ◽  
Heart Rate ◽  
Parkinson's Disease ◽  
Heart Rate Variability ◽  
Meta Analysis ◽  
Cardiac Activity ◽  
Heart Rate Variability Analysis ◽  
Short Term ◽  
Variability Analysis ◽  
Parasympathetic Regulation

Recent evidence suggests that the vagus nerve and autonomic dysfunction play an important role in the pathogenesis of Parkinson’s disease. Using heart rate variability analysis, the autonomic modulation of cardiac activity can be investigated. This meta-analysis aims to assess if analysis of heart rate variability may indicate decreased parasympathetic tone in patients with Parkinson’s disease. The MEDLINE, EMBASE and Cochrane Central databases were searched on 31 December 2020. Studies were included if they: (1) were published in English, (2) analyzed idiopathic Parkinson’s disease and healthy adult controls, and (3) reported at least one frequency- or time-domain heart rate variability analysis parameter, which represents parasympathetic regulation. We included 47 studies with 2772 subjects. Random-effects meta-analyses revealed significantly decreased effect sizes in Parkinson patients for the high-frequency spectral component (HFms2) and the short-term measurement of the root mean square of successive normal-to-normal interval differences (RMSSD). However, heterogeneity was high, and there was evidence for publication bias regarding HFms2. There is some evidence that a more advanced disease leads to an impaired parasympathetic regulation. In conclusion, short-term measurement of RMSSD is a reliable parameter to assess parasympathetically impaired cardiac modulation in Parkinson patients. The measurement should be performed with a predefined respiratory rate.

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