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An efficient deep learning based stress monitoring model through wearable devices for health care applications

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Abstract

Due to the mental stress of the human, the negative effects are known to be recent decades. Early detections of high level stresses are necessary to stop harmful consequences. Studies have proposed on wearable technologies which detect human stress. This study proposes stress detection systems which use physiological signals of people collected by wearable technologies and attached to human bodies. They can carry it during their daily routine. This work’s proposed system includes removal of artifacts in bio signals and feature extractions from these cleaned signals. Since, DL (deep learning) based models are proven to be the best for these analyses, this article uses a random differential GWO (Grey wolf optimization) algorithm for feature extraction and a ML (machine learning) algorithm called RF (random forest) has been used for classification of the human body parameters like activities of the heart, conductance in skins and corresponding accelerometer signals. The proposed stress detection system is implemented with the real time data gathered from 21 participants. This approach can detect the stress of a human and prevent it from early stages with necessary lectures to avoid the negative effects.

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Data available on request from the authors.

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Authors and Affiliations

  1. Vellore Institute of Technology, Chennai, Tamilnadu, India

    P. Prakash & V. Sakthivel

  2. CHRIST (Deemed to be University), Bengaluru, 560029, India

    P. Prabu & R. Sivakumar

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  1. P. Prakash
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  2. P. Prabu
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  3. V. Sakthivel
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  4. R. Sivakumar
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Contributions

Methodology, Software: PP Visualization, Investigation: PP Supervision: SV Writing—Reviewing and Editing: SR

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Correspondence to P. Prakash.

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Prakash, P., Prabu, P., Sakthivel, V. et al. An efficient deep learning based stress monitoring model through wearable devices for health care applications. Opt Quant Electron 56, 200 (2024). https://doi.org/10.1007/s11082-023-05801-w

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