March 9, 2023Neural Computing and ApplicationsOpen Access

Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques

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Why the study?

Stress is a major contributor to health issues, but individuals often ignore symptoms, and most prior stress assessment studies have been confined to laboratory-based controlled environments.

Can machine learning models accurately detect mental stress in automotive drivers using non-invasive physiological signals?

Population

Automotive drivers from the drivedb dataset

Comparison

Six machine learning models (KNN, SVM, DT, LR, RF, and MLP) to classify stressed vs relaxation states

Design

Machine learning classification study

Authors

ASAli I. SiamKafrelsheikh University SGSamah A. GamelDamietta University FTFatma M. TalaatKafrelsheikh University

Discussion

Member takes

Implication

May aid non-invasive stress monitoring in drivers; hypothesis-generating and requires prospective validation before practice change.

Structured PICO

Can machine learning models accurately detect mental stress in automotive drivers using non-invasive physiological signals?

Population

Automotive drivers (data from drivedb dataset)

Intervention

Machine learning models (KNN, SVM, DT, LR, RF, and MLP) for stress detection based on non-invasive physiological signals (ECG, EMG, GSR, and respiration rate)

Comparator

Comparison among different machine learning models

Outcome

Classification accuracy, sensitivity, and specificity between stressed and relaxation statessurrogate

A Random Forest classifier can accurately detect mental stress in drivers using non-invasive physiological signals with 98.2% accuracy.

Cite This Study

Siam et al. (2023) studied this question.

synapsesocial.com/papers/6a02e8d6a7089d6435652803 https://doi.org/10.1007/s00521-023-08428-w

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March 9, 2023Neural Computing and ApplicationsOpen Access

Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques

View Full Paper

Ask AI

Bookmark

Why the study?

Stress is a major contributor to health issues, but individuals often ignore symptoms, and most prior stress assessment studies have been confined to laboratory-based controlled environments.

Can machine learning models accurately detect mental stress in automotive drivers using non-invasive physiological signals?

Population

Automotive drivers from the drivedb dataset

Comparison

Six machine learning models (KNN, SVM, DT, LR, RF, and MLP) to classify stressed vs relaxation states

Design

Machine learning classification study

Authors

ASAli I. SiamKafrelsheikh University SGSamah A. GamelDamietta University FTFatma M. TalaatKafrelsheikh University

Discussion

Member takes

Implication

May aid non-invasive stress monitoring in drivers; hypothesis-generating and requires prospective validation before practice change.

Structured PICO

Can machine learning models accurately detect mental stress in automotive drivers using non-invasive physiological signals?

Population

Automotive drivers (data from drivedb dataset)

Intervention

Machine learning models (KNN, SVM, DT, LR, RF, and MLP) for stress detection based on non-invasive physiological signals (ECG, EMG, GSR, and respiration rate)

Comparator

Comparison among different machine learning models

Outcome

Classification accuracy, sensitivity, and specificity between stressed and relaxation statessurrogate

A Random Forest classifier can accurately detect mental stress in drivers using non-invasive physiological signals with 98.2% accuracy.

Cite This Study

Siam et al. (2023) studied this question.

synapsesocial.com/papers/6a02e8d6a7089d6435652803 https://doi.org/10.1007/s00521-023-08428-w

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

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