Related Experiment Video
Updated: Nov 3, 2025
12:51
Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students
Published on: June 16, 2018
7.7K
An Explainable Machine Learning Approach Based on Statistical Indexes and SVM for Stress Detection in Automobile
Olivia Vargas-Lopez1, Carlos A Perez-Ramirez2, Martin Valtierra-Rodriguez1
1ENAP-Research Group, CA-Sistemas Dinámicos y Control, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro (UAQ), Río Moctezuma 249, San Juan del Rio 76807, Mexico.
Sensors (Basel, Switzerland)
|June 2, 2021
Summary
Detecting driver stress is crucial for preventing car accidents. Statistical time features and support vector machines effectively identify stress events, improving road safety.
Area of Science:
- * Driver safety and automotive engineering.
- * Biomedical signal processing and machine learning applications.
Background:
- * Increasing car accidents are linked to driver stress, necessitating effective stress detection methods.
- * Electromyographical signals (EMG) offer insights into physiological stress responses.
- * Statistical Time Features (STFs) can capture subtle signal changes indicative of stress.
Purpose of the Study:
- * To investigate the efficacy of STFs in detecting driver stress using EMG signals.
- * To evaluate different machine learning classifiers and kernels for stress event detection.
- * To explore model explainability for enhanced comprehension of algorithm performance.
Main Methods:
- * Analysis of electromyographical signals from drivers.
- * Extraction and application of various statistical time features (e.g., variance, standard deviation).
- * Implementation and comparison of Support Vector Machine (SVM) classifiers with different kernels (e.g., cubic).
Main Results:
- * Variance and standard deviation were identified as highly effective STFs for stress detection.
- * A Support Vector Machine classifier with a cubic kernel achieved an Area Under the Curve (AUC) of 0.97.
- * Different SVM kernels demonstrated varying efficacy when trained with STFs, impacting model selection.
Conclusions:
- * STFs, particularly variance and standard deviation, are effective for detecting driver stress events.
- * SVM with a cubic kernel provides high accuracy in identifying stress using EMG-derived features.
- * Model explainability is crucial for understanding algorithm performance and selecting appropriate machine learning models for driver stress detection.
