Abstract
Detecting mental stress is critical for timely intervention and support, especially in groups with distinct pressures, such as housewives. This study investigates the possibility of detecting mental stress in Indian housewives using wearable physiological sensors (separately and combinedly) and deep learning (DL) techniques, notably proposed Recurrent Neural Networks (RNN) and proposed Long Short-Term Memory (LSTM) classifiers. Electrocardiography (ECG), galvanic skin response (GSR), and Skin Temperature (ST) are among the physiological signals studied. These signals provide information on autonomic nervous system regulation, emotional arousal, and changes in peripheral blood flow caused by stress. Notably, feature selection methods have a significant effect on model’s performance. The SelectKBest and Recursive Feature Elimination (RFE) approaches demonstrate promising results in terms of precision, recall, F1-score, and accuracy achieving highest accuracy of 97.51% in LSTM using RFE and 94.23% in RNN using RFE when all data signals collected are used. This study illustrates the importance of wearable sensors for assessing mental stress in Indian housewives, highlighting DL's potential for improving stress detection. This research promises personalized therapy, which will improve mental health and quality of life. Early stress diagnosis and response can help to reduce negative health outcomes. The findings emphasise the significance of feature selection and provide significant insights for future research.