Key result
IBK classifier outperforms alternative models to detect mental stress from physiological signals with 99.5% accuracy.
Why the study?
Mental stress negatively impacts quality of life, and accurate unobtrusive detection using wearable physiological sensors may help deliver prevention and treatment.
A machine learning model using heart rate, blood pressure, and respiration rate can accurately detect mental stress stages, which may have applications in driver safety and smart healthcare.
Wearable stress detection models remain hypothesis-generating; leaves open validation before cardiovascular risk applications.
Mental stress, a psychophysiological state, has a negative impact on a person’s quality of life. To ameliorate mental and physical health, accurate detection of mental state can be propitious to deliver prevention and treatment for this disease. Subsequently, unobtrusive monitoring of physiological signals is made possible by deploying wearables in everyday life. In this context, this paper proposes a stress detection model by incorporating wearable sensors for detection purposes. Stress is detected using three crucial physiological sensors: blood pressure, heart rate, and breathing rate in this study. A methodology supporting five classifiers, capable of predicting cognitive degradation in performance is proposed to classify mental stress into five stages. In terms of accuracy, precision, f-measure, and recall, the IBK classifier outperforms all the other classifiers adopted in the simulation process. This model’s capacity to covertly identify mental stress may aid in reducing the number of accidents and lost productivity on the roadways.
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Karandeep Kaur (2026) studied Mental stress. IBK classifier using physiological signals (BP, HR, RR) vs. Other machine learning classifiers (LR, NB, Trees, Bagging) was evaluated on Classification accuracy. The proposed IBK classifier model achieved a classification accuracy of 99.5% in detecting mental stress stages from physiological signals, outperforming other evaluated classifiers.
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