Driver Stress Detection in Simulated Driving Scenarios with Photoplethysmography

Mateos-García, Nuria; González, Ana Belén Gil; Reboredo, Ana de Luis; Pérez-Lancho, Belén

doi:10.1007/978-3-031-20859-1_29

Cited by 2 publications

(2 citation statements)

References 45 publications

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“…Another research project turned to a traffic surveillance system developed to detect and warn the vehicle driver of a degree of drowsiness or stress [46][47][48]. A smartphone with a mobile application, using the Android operating system, was used to implement a human-computer interaction system.…”

Section: Of 19mentioning

confidence: 99%

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks

Doniec¹,

Konior²,

Sieciński³

et al. 2023

Preprint

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To drive safely, the driver must be aware of the surroundings, pay attention to the road traffic, and be ready to adapt to new circumstances. Most studies on driving safety focus on detecting anomalies in driver behavior and monitoring the cognitive capabilities of drivers. In our study, we proposed a classifier for basic activities in driving a car, based on a similar approach that could be applied to the recognition of basic activities in daily life, that is, using electrooculographic (EOG) signals and a one-dimensional convolutional neural network (1D CNN). Our classifier achieved an accuracy of 80% for the 16 primary and secondary activities. The accuracy related to primary activities in driving, including crossroad, parking, roundabout was 97.9%, 96.8%, 97.4%, and 99.5%, respectively. The F1 score for secondary driving actions (0.99) was higher than for primary driving activities (0.93–0.94). Furthermore, using the same algorithm, it was possible to distinguish four secondary activities related to activities of daily life and secondary when driving a car.

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Section: Of 19mentioning

confidence: 99%

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks

Doniec¹,

Konior²,

Sieciński³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Another research project turned to a traffic surveillance system developed to detect and warn the driver of a degree of drowsiness or stress [ 45 , 46 , 47 ]. A smartphone with a mobile application, using the Android operating system, was used to implement a human–computer interaction system.…”

Section: Related Workmentioning

confidence: 99%

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks

Doniec

Konior

Sieciński

et al. 2023

Sensors

View full text Add to dashboard Cite

To drive safely, the driver must be aware of the surroundings, pay attention to the road traffic, and be ready to adapt to new circumstances. Most studies on driving safety focus on detecting anomalies in driver behavior and monitoring cognitive capabilities in drivers. In our study, we proposed a classifier for basic activities in driving a car, based on a similar approach that could be applied to the recognition of basic activities in daily life, that is, using electrooculographic (EOG) signals and a one-dimensional convolutional neural network (1D CNN). Our classifier achieved an accuracy of 80% for the 16 primary and secondary activities. The accuracy related to activities in driving, including crossroad, parking, roundabout, and secondary activities, was 97.9%, 96.8%, 97.4%, and 99.5%, respectively. The F1 score for secondary driving actions (0.99) was higher than for primary driving activities (0.93–0.94). Furthermore, using the same algorithm, it was possible to distinguish four activities related to activities of daily life that were secondary activities when driving a car.

show abstract

Driver Stress Detection in Simulated Driving Scenarios with Photoplethysmography

Cited by 2 publications

References 45 publications

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks

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