Analyzing mental stress in Indian students through advanced machine learning and wearable technologies.

Mental stress is a prevalent issue in modern society, and detecting and classifying it accurately is crucial for effective interventions and treatment plans. This study aims to compare various machine learning (ML) algorithms for detecting mental stress using wearable physiological signal data and proposes a novel model that is automatic, high-performing, low-cost, and with lower time and computation complexity. The proposed model was trained and tested on a dataset of 200 participants, which involves applying four different stressors. Nine ML algorithms were investigated for both multivariate and univariate features. The physiological data was collected using a novel device developed using an Arduino microcontroller and low-cost sensors such as ECG, GSR, and ST sensors. The findings reveal that the suggested model detects mental stress with an accuracy of 96.17%, with the XGBoost method outperforming other algorithms in multivariate analysis. Univariate feature analysis found that XGBoost regularly demonstrated good accuracy, showing its dependability for detecting mental stress. The novel device created using low-cost sensors and automatic, high-performing algorithms is an effective and accessible tool for mental stress detection. Additionally, benchmark dataset validation (SWELL-KW, WESAD) confirmed the model's robustness with accuracies of 92.38% and 94.21% respectively. A real-time pilot test on ten new participants utilizing the developed device validated the model's practical value, with 97.5% classification accuracy and low latency. This study provides insights into the most effective ML algorithms for mental stress detection and creates a comprehensive and reliable resource for future research.