Driver’s stress detection using Skin Potential Response signals

Affanni, Antonio; Bernardini, R.; Piras, Alessandro; Rinaldo, R.; Zontone, Pamela

doi:10.1016/j.measurement.2018.03.040

Cited by 53 publications

(43 citation statements)

References 96 publications

(99 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Khattak et al carried out statistical analysis (Wilcoxon Signed rank test, t-test and analysis of variance (ANOVA)) on the driver stress based on drivers' HRV [28]. Electrodermal activity (EDA) skin potential response (SPR) is another typical input for the driver stress detection, which an example was shown along with adaptive filtering and spike detection techniques [29]. Skin conductance and EEG served as inputs of the driver stress condition, which were further explored by the incremental association Markov blanket algorithm for feature extraction [30].…”

Section: Existing Work Of Driver Stress Recognitionmentioning

confidence: 99%

“…Various methods have been proposed for driver drowsiness recognition [16][17][18][19][20][21][22][23][24][25][26][27] and driver stress recognition [28][29][30][31][32][33][34][35]. Nonetheless, there exist several challenges and limitations that require further research.…”

Section: Research Gaps and Motivationmentioning

confidence: 99%

“…Consider driver stress recognition, Table 3 compares the performance between the proposed algorithm and related works [28][29][30][31][32][33][34][35], towards a category of input signals, dataset, methodology, types of cross-validation and performance. Similar to driver drowsiness recognition, related works in driver stress recognition experienced similar issues.…”

Section: Comparisons To Related Workmentioning

confidence: 99%

See 2 more Smart Citations

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

Chui

Lytras

Liu

2020

Sensors

View full text Add to dashboard Cite

Driver drowsiness and stress are major causes of traffic deaths and injuries, which ultimately wreak havoc on world economic loss. Researchers are in full swing to develop various algorithms for both drowsiness and stress recognition. In contrast to existing works, this paper proposes a generic model using multiple-objective genetic algorithm optimized deep multiple kernel learning support vector machine that is capable to recognize both driver drowsiness and stress. This algorithm simplifies the research formulations and model complexity that one model fits two applications. Results reveal that the proposed algorithm achieves an average sensitivity of 99%, specificity of 98.3% and area under the receiver operating characteristic curve (AUC) of 97.1% for driver drowsiness recognition. For driver stress recognition, the best performance is yielded with average sensitivity of 98.7%, specificity of 98.4% and AUC of 96.9%. Analysis also indicates that the proposed algorithm using multiple-objective genetic algorithm has better performance compared to the grid search method. Multiple kernel learning enhances the performance significantly compared to single typical kernel. Compared with existing works, the proposed algorithm not only achieves higher accuracy but also addressing the typical issues of dataset in simulated environment, no cross-validation and unreliable measurement stability of input signals. Driver drowsiness is about the sleepiness of drivers that is an undesired condition in real-world driving. Surveys revealed that more than half of adult drivers felt sleepy while driving and even more severe that about 30% of them fell asleep [3]. This high prevalence can further estimate that drivers could experience accidents (affect or being influenced attributable to drowsy driving). It is worth mentioning that driver drowsiness is differed from driver fatigue [4]. Drivers drive unconsciously in the former condition but are with a conscious status in latter condition. In reality, people are usually fatigued in today's fast paced world.Stress is body's way to react any kind of threat, challenge and demand. Research has revealed that stress has played a crucial role in adapting to driving and making decision [5]. The primary sources for driver stress are congestion and adverse driving condition as well as time pressure [6]. The stress can lead to poor and dangerous driving behaviors, for instance, flashing high beams, eliciting anger in drivers, road rage and aggressive driving, which are major causes of road traffic accidents [7]. Various recent studies have been carried out on the investigation between psychological factors and driving behaviors. In [8], results revealed that anger leads to stress, which is reflecting in the form of aggressive and negative cognitive driving behavior. On the other hand, research argued that risky drivers generally exhibited more antisocial and substance misuse, reward sensitive personality features and sensation seeking [9]. In addition, traffic penalties reported by public transp...

show abstract

Section: Existing Work Of Driver Stress Recognitionmentioning

confidence: 99%

Section: Research Gaps and Motivationmentioning

confidence: 99%

Section: Comparisons To Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

Chui

Lytras

Liu

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Moreover, user's emotional state associated with a training session was not evaluated. Biosignals data processing allows looking for clues related to poor driving performance connected to that emotional state [19]- [22].…”

mentioning

confidence: 99%

“…This architecture allows a remote support by the therapist to define customized protocols using virtual (static and animated) objects to be rendered in different markers. During the entire training session, user's biosignals (Electrodermal Activity -EDA and Blood Volume Pulse -BVP) were collected to assess the impacts of the different protocols on each user and provide clues related to performance improvements [19]- [22]. This paper is organized as follows.…”

mentioning

confidence: 99%

The Augmented Reality Telerehabilitation System for Powered Wheelchair User’s Training

Caetano

Valentini²,

Mattioli

et al. 2020

JCIS

View full text Add to dashboard Cite

Many people worldwide have been experimenting a decrease in their mobility as a result of aging, accidents and degenerative diseases. In many cases, a Powered Wheelchair (PW) is an alternative help. Currently in Brazil, patients can receive a PW from the Unified Health System, following prescription criteria. However, they do not have an appropriate previous training for driving the PW. Consequently, users might suffer accidents since a customized training protocol is not available. Nevertheless, due to financial and/or health limitations, many users are unable to attend a rehabilitation center. To overcome these limitations, we developed an Augmented Reality Telerehabilitation System Architecture based on the Power Mobility Road Test (PMRT) for supporting PW user's training. In this system, the therapists can remotely customize and evaluate training tasks and the user can perform the training in safer conditions. The video stream and data transfer between each environment were made possible through UDP (User Datagram Protocol). To evaluate and present the system architecture potential, a preliminary test was conducted with 3 spinal cord injury participants. They performed 3 basic training protocols defined by a therapist. The following metrics were adopted for evaluation: number of control commands; elapsed time; number of collisions; biosignals. Also, a questionary was used to evaluate system features. Preliminary results demonstrated the specific needs of individuals using a PW thanks to adopted metrics (qualitative and emotional). Results have shown the need for a training system with customizable protocols to fulfill these needs. User's evaluation demonstrates that the combination of AR techniques with PMRT adaptations, increases user's well-being after training sessions. Furthermore, a training experience helps users to overcome their displacement problems, as well as for appointing challenges before large scale use. This system architecture allows further studies on telerehabilitation of PW users training.

show abstract