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<p>In the current century, most human beings are distressed by stress. The stress impacts the mental and physical health of people and causes a high influence on human health. The stress affects the daily activities of human life like academics and work. Moreover, stress causes illnesses like anxiety, headaches, heart disease, and depression. Hence, it is essential to avoid these kinds of negative issues due to the high level of stress. The earlier stage of stress detection is necessary to reduce the stress level and provide proper treatment to patients. In this paper, the hybrid deep learning (DL) model called cascaded neuro-fuzzy SpinalNet (CNFSNet)–based human stress level detection model is proposed. The min–max normalization–enabled data normalization is the initial process, in which the data is normalized. Moreover, the essential features from the data are augmented to enhance the data sizes. The fuzzy local information cluster means (FLICM) is employed for feature clustering. At last, the detection of stress levels is performed using the CNFSNet. The accuracy, precision, and recall metrics are used to estimate the CNFSNet-based stress level detection model, with the outcomes of 0.9012, 0.906, and 0.902 achieved.</p>

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Human Stress Level Detection Using Hybrid Cascaded Neuro-Fuzzy SpinalNet

  • 期刊论文

  • 发表日期：

    2025年7月18日

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  • P. Lakshmi,
  • Manoj Kumar G.,
  • Smitha Vas P.,
  • Baiju P. S,
  • Senthilnathan Chidambaranathan

摘要

In the current century, most human beings are distressed by stress. The stress impacts the mental and physical health of people and causes a high influence on human health. The stress affects the daily activities of human life like academics and work. Moreover, stress causes illnesses like anxiety, headaches, heart disease, and depression. Hence, it is essential to avoid these kinds of negative issues due to the high level of stress. The earlier stage of stress detection is necessary to reduce the stress level and provide proper treatment to patients. In this paper, the hybrid deep learning (DL) model called cascaded neuro-fuzzy SpinalNet (CNFSNet)–based human stress level detection model is proposed. The min–max normalization–enabled data normalization is the initial process, in which the data is normalized. Moreover, the essential features from the data are augmented to enhance the data sizes. The fuzzy local information cluster means (FLICM) is employed for feature clustering. At last, the detection of stress levels is performed using the CNFSNet. The accuracy, precision, and recall metrics are used to estimate the CNFSNet-based stress level detection model, with the outcomes of 0.9012, 0.906, and 0.902 achieved.