A comparative evaluation of neural network classifiers for stress level analysis of automotive drivers using physiological signals

Singh, Rajiv Ranjan; Conjeti, Sailesh; Banerjee, Rahul

doi:10.1016/j.bspc.2013.06.014

Cited by 147 publications

(65 citation statements)

References 22 publications

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“…This may be particularly relevant for those jobs that expose the workers or other persons to risky situations. This is the case of surgeons performing long surgeries, in which the loss of attention or concentration may cause severe effects on the patient, or pilots flying on long distances, whose stress may be dangerous for them and the passengers [7].…”

Section: Introductionmentioning

confidence: 99%

Acute mental stress assessment via short term HRV analysis in healthy adults: A systematic review with meta-analysis

Castaldo

Melillo

Bracale

et al. 2015

Biomedical Signal Processing and Control

355

249

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Leandro. (2015) Acute mental stress assessment via short term HRV analysis in healthy adults : a systematic review with meta-analysis. Biomedical Signal Processing and Control, Volume 18 . pp. 370-377. Permanent WRAP url:http://wrap.warwick.ac.uk/69425 Copyright and reuse:The Warwick Research Archive Portal (WRAP) makes this work by researchers of the University of Warwick available open access under the following conditions. Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable the material made available in WRAP has been checked for eligibility before being made available.Copies of full items can be used for personal research or study, educational, or not-forprofit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP url' above for details on accessing the published version and note that access may require a subscription.For more information, please contact the WRAP Team at: publications@warwick.ac.uk Acute mental stress assessment via short term HRV analysis in healthy adults: a systematic review with meta-analysis. AbstractMental stress reduces performances, on the work place and in daily life, and is one of the first causes of cognitive dysfunctions, cardiovascular disorders and depression. This study systematically reviewed existing literature investigating, in healthy subjects, the associations between acute mental stress and short-term Heart Rate Variability (HRV) measures in time, frequency and non-linear domain. The goal of this study was to provide reliable information about the trends and the pivot values of HRV measures during mental stress. A systematic review and meta-analysis of the evidence was conducted, performing an exhaustive research of electronic repositories and linear researching references of papers responding to the inclusion criteria. After removing duplicates and not pertinent papers, journal papers describing well-designed studies that analyzed rigorously HRV were included if analyzed the same population of healthy subjects at rest and during mental stress. 12 papers were shortlisted, enrolling overall 758 volunteers and investigating 22 different HRV measures, 9 of which reported by at least 2 studies and therefore meta-analyzed in this review. Four measures in time and nonlinear domains, associated with a normal degree of HRV variations resulted significantly depressed during stress. The power of HRV fluctuations at high frequencies was significantly depressed during stress, while the ratio between low and high frequency resulted significantly increased, suggest...

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Section: Introductionmentioning

confidence: 99%

Acute mental stress assessment via short term HRV analysis in healthy adults: A systematic review with meta-analysis

Castaldo

Melillo

Bracale

et al. 2015

Biomedical Signal Processing and Control

355

249

View full text Add to dashboard Cite

show abstract

“…A lapse of attention in any one of these environments will cause human casualties or/and property losses. Commonly, there are three ways to monitor the vigilance level: the methods based on behavior performance Wu and Chen;Azim et al, 2014;Cyganek & Gruszczyń ski, 2014;Jo, Lee, Park, Kim, & Ki, 2014;McIntire, McKinley, Goodyear, & McIntire, 2014) and the analysis based on physiological signals (Borghini et al, 2014;Dissanayaka et al, 2015;Garcés Correa et al, 2014;Hashemi et al, 2014;Jagannath & Balasubramanian, 2014;Lee et al, 2014;Li & Chung, 2014;Singh et al, 2013).…”

Section: Review Of the Similar Workmentioning

confidence: 99%

“…A great number of ''physiological signals based method'' for drowsiness detection have been studied (Borghini et al, 2014;Dissanayaka et Li & Chung, 2014;Singh et al, 2013). Li and Chung (2014) presented a novel way to compute the eye closure degree (ECD) using EEG sensors instead of video-based methods, which not only addressed the video-based method drawbacks, but also made ECD estimation more computationally efficient and easier to implement in EEG sensors in a real time way.…”

Section: Review Of the Similar Workmentioning

confidence: 99%

“…Physiological measurements such as EEG recordings, eyelid movement, galvanic skin response (GSR), heart rate and pulse rate provide insight into human activities directly and achieve relatively accurate quantification evaluation of drowsiness/alertness (Boksem et al, 2005;Borghini et al, 2014;Dissanayaka et al, 2015;Garcés Correa et al, 2014;Hashemi et al, 2014;Hu & Zheng, 2009;Jagannath & Balasubramanian, 2014;Lal & Craig, 2005;Lee et al, 2014;Li & Chung, 2014;Singh et al, 2013). Among these different measures, EEG is commonly viewed as the ''golden standard'' in the identification of states ranging from vigilant or alert to drowsy or asleep (Garcés Correa et al, 2014;Johnson et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Garcés Correa et al (2014) developed an automatic method to detect the drowsy status in EEG records based on time, spectral and wavelet analysis and the selected features were fed to a neural network classifier. Artificial neural network (ANN) (Garcés Correa et al, 2014;Singh et al, 2013;Subasi, 2005;Vuckovic et al, 2002) and support vector machine (SVM) (Hu & Zheng, 2009;Jo et al, 2014;Lal & Craig, 2005;Lee et al, 2014;Yeo et al, 2009;Zhao, Zheng, Zhao, Tu, & Liu, 2011) have been widely explored as the detector for classification. However, when the sample size is large, the learning speed of both methods is too slow to meet the requirements for real-time applications.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic detection of alertness/drowsiness from physiological signals using wavelet-based nonlinear features and machine learning

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Expert Systems with Applications

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Intelligent In‐Vehicle Interaction Technologies

Murali

Kaboli

Dahiya

2021

Advanced Intelligent Systems

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Autonomous vehicles (AVs) will soon become a reality with almost every major automotive original equipment manufacturer (OEM) such as Tesla, BMW, Daimler, and big technology giants such as Google's Waymo and Apple Car pushing toward the full self-driving objective. According to the SAE J3016 taxonomy [1] of autonomous driving, there are six levels of automated driving systems ranging from level 0 (completely manual) to level 5 (full selfdriving [FSD]) automated systems that are expected to function in all geographic locations, all weather conditions, and under all conditions. The proposed benefits of intelligent vehicles include fewer road accidents, enhanced safety, reduced traffic congestion, effective commute time usage, and importantly enjoyable and comfortable ride. With increased autonomy, the drivers also assume the role of mere passengers who are engaged in nondriving activities and are unavailable to participate in traffic interactions. This would increase complexity in a mixed-autonomy traffic environment as interactions with pedestrians and cyclists are based on visual cues with the driver. Therefore, intelligent vehicles also need to autonomously interact with other traffic participants such as pedestrians and cyclists and other vehicles.Human-vehicle interaction (HVI) is closely related to the field of human-robot interaction (HRI). It entails the problem of understanding and shaping the interaction dynamics between humans and vehicles. Particularly, the domain of interaction involves the study of sensation, perception, information exchange, inference, and decision-making. [2] With increasing levels of automation, the driver will have more time and choice to perform various tasks other than driving and this opens new avenues for interaction. As a result, a growing number of sensors are being incorporated into the vehicles to understand the driver's and/or passenger's actions, emotions, and personal choices to offer the precise functionalities and services for an enjoyable ride. [3] Figure 1 shows the schematic of in-vehicle interaction consisting of various interactive interfaces and sensing modalities present in the vehicle as well as some modes of interaction such as gestures, speech, and eye gaze.The advances in HVI have been presented in previous review articles, which have mainly focused on the detection of particular characteristics for driving assistance such as driver attention detection, [4] emotion recognition, [5] drowsiness detection, [6] mental workload, [7] and so on. A few other articles have reviewed the interior designs of AVs to better support and interact with drivers and passengers. [8,9] Likewise, the user interfaces (UIs) and user experience (UX) of vehicle interiors have been studied in context with manual as well as automated driving. [10] A number of previous works have also studied the interaction with the external

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A comparative evaluation of neural network classifiers for stress level analysis of automotive drivers using physiological signals

Cited by 147 publications

References 22 publications

Acute mental stress assessment via short term HRV analysis in healthy adults: A systematic review with meta-analysis

Acute mental stress assessment via short term HRV analysis in healthy adults: A systematic review with meta-analysis

Automatic detection of alertness/drowsiness from physiological signals using wavelet-based nonlinear features and machine learning

Intelligent In‐Vehicle Interaction Technologies

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