article · Neural Computing and Applications
This research addresses the issue of unrecognised stress, a major contributor to health problems, by proposing an automatic method for detecting mental stress in car drivers. Unlike many laboratory-based studies, this approach uses non-invasive biosignals, specifically ECG, EMG, GSR, and respiration rate, collected from drivers. The developed stress detection technique involves three stages: biosignal pre-processing, feature extraction, and classification using machine learning models. Six different machine learning algorithms were evaluated to distinguish between stressed and relaxed states. The Random Forest classifier demonstrated the highest performance, achieving a classification accuracy of 98.2%, 97% sensitivity, and 100% specificity on the drivedb dataset.
Unrecognised stress can severely impact health and safety, particularly for drivers, increasing accident risks. This research offers a method for automatic stress detection, which could enable timely interventions. By alerting drivers to their stress levels, it could help prevent accidents and promote better well-being on the road.
This research presents an applied technique for automatic driver stress detection using non-invasive biosignals and machine learning. Such a system could be integrated into existing Driver Assistance Systems (DAS) in vehicles, providing real-time stress monitoring. Potential users include car manufacturers for enhanced safety features and individual drivers for personal well-being. This appears to be applied research with a clear pathway towards product integration, though not yet a near-market solution.
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Abstract Stress is now thought to be a major cause to a wide range of human health issues. However, many people may ignore their stress feelings and disregard to take action before serious physiological and mental disorders take place. The heart rate (HR) and blood pressure (BP) are the most physiological markers used in various studies to detect mental stress for a human, and because they are captured non-invasively using wearable sensors, these markers are recommended to provide information on a person’s mental state. Most stress assessment studies have been undertaken in a laboratory-based controlled environment. This paper proposes an approach to identify the mental stress of automotive drivers based on selected biosignals, namely, ECG, EMG, GSR, and respiration rate. In this study, six different machine learning models (KNN, SVM, DT, LR, RF, and MLP) have been used to classify between the stressed and relaxation states. Such system can be integrated with a Driver Assistance System (DAS). The proposed stress detection technique (SDT) consists of three main phases: (1) Biosignal Pre-processing, in which the signal is segmented and filtered. (2) Feature Extraction, in which some discriminate features are extracted from each biosignal to describe the mental state of the driver. (3) Classification. The results show that the RF classifier outperforms other techniques with a classification accuracy of 98.2%, sensitivity 97%, and specificity 100% using the drivedb dataset.
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DOI: 10.1007/s00521-023-08428-w
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