ArticleSensors (Basel, Switzerland)2021
An Explainable Machine Learning Approach Based on Statistical Indexes and SVM for Stress Detection in Automobile Drivers Using Electromyographic Signals.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A Bio-Inspired Physiological-Behavioral Fusion Method for Human-Factor Risk Assessment of Imported Unmanned Intelligent System Operators in Port-Security Scenarios.Biomimetics (Basel, Switzerland) · 2026Article
- Towards accurate transcutaneous COBiosensors & bioelectronics · 2026Article
- Bone marrow CCR3 dictates eosinophil lineage commitment of CD34⁺ progenitors to orchestrate allergic rhinitis: A composite study.PloS one · 2026Article
- Machine learning and EEG can classify passive viewing of discrete categories of visual stimuli but not the observation of pain.BMC neuroscience · 2023Article
- Global Stress Detection Framework Combining a Reduced Set of HRV Features and Random Forest Model.Sensors (Basel, Switzerland) · 2023Article
- Real-World Driver Stress Recognition and Diagnosis Based on Multimodal Deep Learning and Fuzzy EDAS Approaches.Diagnostics (Basel, Switzerland) · 2023Article
- Driver Stress Detection Using Ultra-Short-Term HRV Analysis under Real World Driving Conditions.Entropy (Basel, Switzerland) · 2023Article
- External validation of binary machine learning models for pain intensity perception classification from EEG in healthy individuals.Scientific reports · 2023Article
- Pilot Behavior Recognition Based on Multi-Modality Fusion Technology Using Physiological Characteristics.Biosensors · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The economic and personal consequences that a car accident generates for society have been increasing in recent years. One of the causes that can generate a car accident is the stress level the driver has; consequently, the detection of stress events is a highly desirable task. In this article, the efficacy that statistical time features (STFs), such as root mean square, mean, variance, and standard deviation, among others, can reach in detecting stress events using electromyographical signals in drivers is investigated, since they can measure subtle changes that a signal can have. The obtained results show that the variance and standard deviation coupled with a support vector machine classifier with a cubic kernel are effective for detecting stress events where an AUC of 0.97 is reached. In this sense, since SVM has different kernels that can be trained, they are used to find out which one has the best efficacy using the STFs as feature inputs and a training strategy; thus, information about model explain ability can be determined. The explainability of the machine learning algorithm allows generating a deeper comprehension about the model efficacy and what model should be selected depending on the features used to its development.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.