sis 18: e2

Research Article

An Improved Approach for Stress Detection Using Physiological Signals

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  • @ARTICLE{10.4108/eai.14-5-2021.169919,
        author={Kushagra Nigam and Kirti Godani and Deepshi Sharma and Shikha Jain},
        title={An Improved Approach for Stress Detection Using Physiological Signals},
        journal={EAI Endorsed Transactions on Scalable Information Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SIS},
        year={2021},
        month={5},
        keywords={Stress Detection, Electro dermal Activity (EDA), 3-axis Acceleration (ACC), Body Temperature (TEMP), Long Short-Term Memory Network (LSTM), Wearable and Stress Affect Detection (WESAD)},
        doi={10.4108/eai.14-5-2021.169919}
    }
    
  • Kushagra Nigam
    Kirti Godani
    Deepshi Sharma
    Shikha Jain
    Year: 2021
    An Improved Approach for Stress Detection Using Physiological Signals
    SIS
    EAI
    DOI: 10.4108/eai.14-5-2021.169919
Kushagra Nigam1, Kirti Godani1, Deepshi Sharma1, Shikha Jain1,*
  • 1: Jaypee Institute of Information Technology, India
*Contact email: shi_81@rediffmail.com

Abstract

Stress is a major problem in society. Prolonged stress can lead to ill-health and a decrease in self-confidence. It is necessary to detect stress at an early stage to prevent its adverse effects on our physical and psychological health. The paper presents a stress detection model using physiological signals. In this paper, WESAD (Wearable and Stress Affect Detection) dataset is used which consists of physiological data recorded from both the chest and wrist. Further, a Long Short-Term Memory (LSTM) based model is used to detect stress. The simulation results indicate that, indeed, Electrocardiograph (ECG), Electromyogram (EMG), and Respiration (RESP) signals may not be necessary for identifying stress. A three-way validation is carried out with an accuracy of 98%. The novelty of the paper is the way time-series data is handled to make it closer to real-time data captured from sensors. The work can be used widely in clinical practices to detect stress at an early stage.