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<p>Detecting mental stress is critical for appropriate involvement and support, especially in groups with distinct pressures and burdens, such as housewives. This study investigates the prospect of detecting mental stress in Indian housewives using wearable physiological sensors (separately and combinedly) and deep learning (DL) techniques -proposed Recurrent Neural Networks (RNN) and proposed Long Short-Term Memory (LSTM) classifiers. Electrocardiography (ECG), Galvanic Skin Response (GSR), and Skin Temperature (ST) are the three physiological signals studied here, providing information on autonomic nervous system regulation, emotional arousal, and changes in peripheral blood flow caused by mental stress. Especially, feature selection methods have a significant effect on the model’s performance. The SelectKBest and Recursive Feature Elimination (RFE) approaches demonstrate promising results in terms of accuracy, precision, recall, and F1-score, achieving the highest accuracy of 97.51%, precision 96.38%, recall 98.72%, and F1 score 97.67% in LSTM using RFE and accuracy of 94.23%, precision 94.82%, recall 96.23%, and F1 score 95.52% in RNN using RFE when all data signals collected are used. This study explains the importance of wearable sensors for detecting mental stress in Indian housewives, highlighting DL’s potential for improving stress detection. Early stress diagnosis and response can help to reduce negative health outcomes. The findings emphasize the significance of feature selection and provide significant insights for future research.</p>

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A deep learning approach with wearable physiological sensors and feature selection methods to detect mental stress in Indian housewives

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月11日

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  • Shruti Gedam,
  • Prashant Pranav,
  • Sandip Dutta,
  • Ritesh Jha

摘要

Detecting mental stress is critical for appropriate involvement and support, especially in groups with distinct pressures and burdens, such as housewives. This study investigates the prospect of detecting mental stress in Indian housewives using wearable physiological sensors (separately and combinedly) and deep learning (DL) techniques -proposed Recurrent Neural Networks (RNN) and proposed Long Short-Term Memory (LSTM) classifiers. Electrocardiography (ECG), Galvanic Skin Response (GSR), and Skin Temperature (ST) are the three physiological signals studied here, providing information on autonomic nervous system regulation, emotional arousal, and changes in peripheral blood flow caused by mental stress. Especially, feature selection methods have a significant effect on the model’s performance. The SelectKBest and Recursive Feature Elimination (RFE) approaches demonstrate promising results in terms of accuracy, precision, recall, and F1-score, achieving the highest accuracy of 97.51%, precision 96.38%, recall 98.72%, and F1 score 97.67% in LSTM using RFE and accuracy of 94.23%, precision 94.82%, recall 96.23%, and F1 score 95.52% in RNN using RFE when all data signals collected are used. This study explains the importance of wearable sensors for detecting mental stress in Indian housewives, highlighting DL’s potential for improving stress detection. Early stress diagnosis and response can help to reduce negative health outcomes. The findings emphasize the significance of feature selection and provide significant insights for future research.