Stress Events Detection of Driver by Wearable Glove System

Lee, Dae Seok; Chong, Teak Wei; Lee, Boon-Giin

doi:10.1109/jsen.2016.2625323

Cited by 58 publications

(48 citation statements)

References 19 publications

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“…The result indicated that the combination of ECG, EDA, and respiration signals to distinguish drivers' rest from stress has the optimal performance (accuracy: 87.3% ± 2.8) among 88:19 different signal combinations. In another study, SFFS was applied on 20 features extracted from drivers' PPG signals and steering-wheel motion data (Lee et al 2017). The features were separately fed to an SVM-Gaussian kernel function classifier to evaluate their performance.…”

Section: Wrapper Methodsmentioning

confidence: 99%

“…The effects of stress on a driver can be measured indirectly through dynamic data obtained from the vehicle. This data can be collected through different systems such as CAN-Bus (Bořil et al 2012;Rigas et al 2012;Rodrigues et al 2015), GPS tracking 88:11 (Rigas et al 2012), driving simulators (Cantin et al 2009;Tango and Botta 2013), mobile phones (Tchankue et al 2013), and orientation sensors (Lee et al 2017).…”

Section: Vehicle Dynamic Datamentioning

confidence: 99%

“…One study used PPG signals and steering-wheel motion data-captured using a 9-degree-offreedom orientation sensor to detect drivers' stress (Lee et al 2017). In the study, three stress detection models were built using SVM classifiers with different kernel functions.…”

Section: Vehicle Dynamic Datamentioning

confidence: 99%

“…SVM deals with nonlinear problems by using kernel functions that map nonlinear separable data into high-dimensional space, then finds the optimal hyperplanes. SVM classifiers, with three different kernel functions-linear, polynomial, and Gaussianwere used to detect stress of 28 drivers using their PPG signals and steering-wheel motion data (Lee et al 2017). The highest accuracy (95.38%) was obtained using an SVM-Gaussian kernel function classifier.…”

Section: Support Vector Machinesmentioning

confidence: 99%

“…et al (2012),Rigas et al (2012),Lanatà et al (2015), andLee et al (2017) Ü Steering-wheel angle (SWA): wavelet decomposition energy for SWA; standard deviation of SWA (SDSWA), SDSWA rate, sample entropy; area under the SWA curve; number of sign changes of SW; mean and SD of SWA differences between two consecutive skids; reaction time; SWA first peak; SWA second peak; first response time of SWA, first ability time of SWA Ü Steering wheel motion (SWM) captured by orientation sensor (Y-axis): mean, SD, variance, average squared power, median, root mean square, range difference max and mean, sum power spectrum energy (0-8Hz), mean power frequency (0-8Hz), Shannon entropy energy (0-8Hz), peak power frequency (0-8Hz), median power frequency (0-8Hz), percentage of point outside the control eclipse, weighting function outside the control eclipse Ü Speed: wavelet decomposition energy, sample entropy, SD, average Ü Deceleration: average Ü Heading Change: average magnitude in heading changes Ü Throttle: average Ü Engine performance: Engine's rounds per minute…”

mentioning

confidence: 96%

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A Critical Review of Proactive Detection of Driver Stress Levels Based on Multimodal Measurements

et al. 2018

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Stress is a major concern in daily life, as it imposes significant and growing health and economic costs on society every year. Stress and driving are a dangerous combination and can lead to life-threatening situations, evidenced by the large number of road traffic crashes that occur every year due to driver stress. In addition, the rate of general health issues caused by work-related chronic stress in drivers who work in public and private transport is greater than in many other occupational groups. An in-vehicle warning system for driver stress levels is needed to continuously predict dangerous driving situations and proactively alert drivers to ensure safe and comfortable driving. As a result of the recent developments in ambient intelligence, such as sensing technologies, pervasive devices, context recognition, and communications, driver stress can be automatically detected using multimodal measurements. This critical review investigates the state of the art of techniques and achievements for automatic driver stress level detection based on multimodal sensors and data. In this work, the most widely used data followed by frequent and highly performed selected features to detect driver stress levels are analyzed and presented. This review also discusses key methodological issues and gaps that hinder the implementation of driver stress detection systems and offers insights into future research directions. CCS-Concepts: Human-centered computing → Ubiquitous and mobile computing; General and reference → Surveys and overviews; Computing methodologies → Machine learning algorithms

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Section: Wrapper Methodsmentioning

confidence: 99%

Section: Vehicle Dynamic Datamentioning

confidence: 99%

Section: Vehicle Dynamic Datamentioning

confidence: 99%

Section: Support Vector Machinesmentioning

confidence: 99%

mentioning

confidence: 96%

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A Critical Review of Proactive Detection of Driver Stress Levels Based on Multimodal Measurements

et al. 2018

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Trifluralin recognition using touch‐based fingertip: Application of wearable glove‐based sensor toward environmental pollution and human health control

Farshchi

Saadati

Kordasht

et al. 2021

J of Molecular Recognition

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Monitoring of herbicides and pesticides in water, food, and the environment is essential for human health, and this requires low-cost, portable devices for widespread deployment of this technology. For the first time, a wearable glove-based electrochemical sensor based on conductive Ag nano-ink was developed for the on-site monitoring of trifluralin residue on the surface of various substrates. Three electrode system with optimal thicknesses was designed directly on the finger surface of a rubber glove. Then, fabricated electrochemical sensor used for the direct detection of trifluralin in the range of 0.01 μM to 1 mM on the surface of tomato and mulberry leaves using square wave voltammetry (SWV) and difference pulse voltammetry technique. The obtained LLOQ was 0.01 μM, which indicates the suitable sensitivity of this sensor. On the other hand, this sensor is portable, easy to use, and has a high environmental capability that can be effective in detecting other chemical threats in the soil and water environment.

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Methods to Detect and Reduce Driver Stress: A Review

Chung

Chong

Lee

2019

Int.J Automot. Technol.

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Automobiles are the most common modes of transportation in urban areas. An alert mind is a prerequisite while driving to avoid tragic accidents; however, driver stress can lead to faulty decision-making and cause severe injuries. Therefore, numerous techniques and systems have been proposed and implemented to subdue negative emotions and improve the driving experience. Studies show that conditions such as the road, state of the vehicle, weather, as well as the driver's personality, and presence of passengers can affect driver stress. All the above-mentioned factors significantly influence a driver's attention. This paper presents a detailed review of techniques proposed to reduce and recover from driving stress. These technologies can be divided into three categories: notification alert, driver assistance systems, and environmental soothing. Notification alert systems enhance the driving experience by strengthening the driver's awareness of his/her physiological condition, and thereby aid in avoiding accidents. Driver assistance systems assist and provide the driver with directions during difficult driving circumstances. The environmental soothing technique helps in relieving driver stress caused by changes in the environment. Furthermore, driving maneuvers, driver stress detection, driver stress, and its factors are discussed and reviewed to facilitate a better understanding of the topic.

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Stress Events Detection of Driver by Wearable Glove System

Cited by 58 publications

References 19 publications

A Critical Review of Proactive Detection of Driver Stress Levels Based on Multimodal Measurements

A Critical Review of Proactive Detection of Driver Stress Levels Based on Multimodal Measurements

Trifluralin recognition using touch‐based fingertip: Application of wearable glove‐based sensor toward environmental pollution and human health control

Methods to Detect and Reduce Driver Stress: A Review

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