An Improved Approach for Stress Detection Using Physiological Signals

Nigam, Kushagra and Godani, Kirti and Sharma, Deepshi and Jain, Shikha (2021) An Improved Approach for Stress Detection Using Physiological Signals. EAI Transactions on Scalable Information Systems. e11. ISSN 2032-9407

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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.

Item Type: Article
Uncontrolled 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)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Depositing User: EAI Editor IV
Date Deposited: 26 Jul 2021 15:12
Last Modified: 26 Jul 2021 15:12
URI: https://eprints.eudl.eu/id/eprint/5170

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