Evaluation on Diversity of Drivers’ Cognitive Stress Response using EEG and ECG Signals during Real-Traffic Experiment with an Electric Vehicle

Noh, Yuna; Kim, Se-Yun; Yoon, Yoonjin

doi:10.1109/itsc.2019.8916844

Cited by 3 publications

(6 citation statements)

References 14 publications

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“…Recent studies on detecting stress, fatigue, and concentration lapses are based on a combination of biosignals and extra sensors, such as the camera [65], vehicle parameters (steering wheel, gas and brake angles, and speed) [50], weather sensors (rain, fog, light and sun direction) [50], GPS position [61], and traffic information by eCell [58]. Some of the above-mentioned extra sensors have been employed in semi-autonomous vehicle design for predicting hazardous or stressful situations and for generating an automatic break in real experiments [53], [54], [65], [77].…”

Section: ) Hybrid Methods and External Sensors In Stress Identificationmentioning

confidence: 99%

“…Afterwards, Noh et al [77] used the EEG and ECG biosignals to identify stress on three levels and consider productivity in the experimental HEO's tasks. In the algorithm, entropy of frequency-based features from the EEG, HRV, and environmental data were extracted for stress identification.…”

Section: ) Eeg Role In Stress Identificationmentioning

confidence: 99%

“…Afterwards, Noh et al [77] used a combination of sensors to record biosignals, vehicle parameters, and environmental condition data. In the algorithm, the focus is on using the EEG and using unsupervised fuzzy c-means clustering for three levels of stress.…”

Section: B Development Of Stress Identification Algorithms For Vehicle Drivers and Heosmentioning

confidence: 99%

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Biosignals in Human Factors Research for Heavy Equipment Operators: A Review of Available Methods and Their Feasibility in Laboratory and Ambulatory Studies

et al. 2021

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Heavy equipment operation is a responsible and difficult task causing mental workload on a human operator and exposing the operator to a range of harmful factors. Human factors and ergonomics in heavy equipment design have traditionally been focused on anthropometry and questionnaires. More advanced techniques involving biosignal measurements were not applied to heavy equipment, mainly due to the diversity of real working conditions that were hard to reproduce in a laboratory environment and that prevented ambulatory studies. Recent advances in wearable biosensors and real-time simulators produce the capability of using biosignals for improving the ergonomics of heavy equipment operation. The present paper reviews the use of biosignals in human factors and the ergonomics of heavy machines by focusing on stress detection for the last ten years. The aim of the paper is analyzing the previous implemented algorithms to find a set of biosignals and methods of stress identification that could be suitable for identifying stress in heavy equipment operators both in laboratory and ambulatory studies. The conclusion emphasizes successful stress identification methods and a combination of the algorithms from different studies that facilitate the use of heavy equipment operator's applications. Also, feasible methods and directions for future research are considered.

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Section: ) Hybrid Methods and External Sensors In Stress Identificationmentioning

confidence: 99%

Section: ) Eeg Role In Stress Identificationmentioning

confidence: 99%

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Biosignals in Human Factors Research for Heavy Equipment Operators: A Review of Available Methods and Their Feasibility in Laboratory and Ambulatory Studies

et al. 2021

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“…The proposed method scored a classification accuracy of 79%. In the research undertaken in [34], EEG and ECG signals in addition to electric vehicle data were acquired from 40 drivers during real driving tasks to classify drivers' stress level. It was shown that the stress level in the drivers was not only affected by the environment conditions such as the road, traffic, and driving duration, but also by individual patterns.…”

Section: Introductionmentioning

confidence: 99%

“…Even if linearity and/or measurement error are not provided for commercial devices, most of the scientific literature acquires EEG signals using commercial sensors, and the data are then processed with ML algorithms in order to classify the stress in drivers, especially using driving simulators. Some studies used the help of other bio-sensors to categorize and label the acquired EEG signals [33,34] (again, we point out the importance of a precise time alignment between different sensors); some other studies relied on the drivers' selfreport [35]; other studies classified the mental activity using an arbitrary threshold on the EEG signal level and labeling the categories on the basis of threshold trespassing [36]. In this study, we adopted the analysis of the beta activities to identify the stress in drivers from the acquired EEG signals, and this method is well known in the literature [39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Development of an EEG Headband for Stress Measurement on Driving Simulators

Affanni

Najafi

Guerci³

2022

Sensors

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In this paper, we designed from scratch, realized, and characterized a six-channel EEG wearable headband for the measurement of stress-related brain activity during driving. The headband transmits data over WiFi to a laptop, and the rechargeable battery life is 10 h of continuous transmission. The characterization manifested a measurement error of 6 μV in reading EEG channels, and the bandwidth was in the range [0.8, 44] Hz, while the resolution was 50 nV exploiting the oversampling technique. Thanks to the full metrological characterization presented in this paper, we provide important information regarding the accuracy of the sensor because, in the literature, commercial EEG sensors are used even if their accuracy is not provided in the manuals. We set up an experiment using the driving simulator available in our laboratory at the University of Udine; the experiment involved ten volunteers who had to drive in three scenarios: manual, autonomous vehicle with a “gentle” approach, and autonomous vehicle with an “aggressive” approach. The aim of the experiment was to assess how autonomous driving algorithms impact EEG brain activity. To our knowledge, this is the first study to compare different autonomous driving algorithms in terms of drivers’ acceptability by means of EEG signals. The obtained results demonstrated that the estimated power of beta waves (related to stress) is higher in the manual with respect to autonomous driving algorithms, either “gentle” or “aggressive”.

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Vehicle Automation Field Test: Impact on Driver Behavior and Trust

Morales-Alvarez

Smirnov

Matthes

et al. 2020

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)

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With the growing technological advances in autonomous driving, the transport industry and research community seek to determine the impact that autonomous vehicles (AV) will have on consumers, as well as identify the different factors that will influence their use. Most of the research performed so far relies on laboratory-controlled conditions using driving simulators, as they offer a safe environment for testing advanced driving assistance systems (ADAS). In this study we analyze the behavior of drivers that are placed in control of an automated vehicle in a real life driving environment. The vehicle is equipped with advanced autonomy, making driver control of the vehicle unnecessary in many scenarios, although a driver take over is possible and sometimes required. In doing so, we aim to determine the impact of such a system on the driver and their driving performance. To this end road users' behavior from naturalistic driving data is analyzed focusing on awareness and diagnosis of the road situation. Results showed that the road features determined the level of visual attention and trust in the automation. They also showed that the activities performed during the automation affected the reaction time to take over the control of the vehicle.

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Evaluation on Diversity of Drivers’ Cognitive Stress Response using EEG and ECG Signals during Real-Traffic Experiment with an Electric Vehicle

Cited by 3 publications

References 14 publications

Biosignals in Human Factors Research for Heavy Equipment Operators: A Review of Available Methods and Their Feasibility in Laboratory and Ambulatory Studies

Biosignals in Human Factors Research for Heavy Equipment Operators: A Review of Available Methods and Their Feasibility in Laboratory and Ambulatory Studies

Development of an EEG Headband for Stress Measurement on Driving Simulators

Vehicle Automation Field Test: Impact on Driver Behavior and Trust

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