Abstract
In the present time, stress is one of the major problems in human life. Person from every age group is facing stress and have increased major diseases such as distressing, heart diseases, and others. As we know “Prevention is better than cure” so, early recognition of mental stress can avoid many stress-related problems both physically and mentally including heart stroke and depression. When a person is stressed, many biological signals such as heat, electricity, impedance, acoustics, and optics change take place in their body and these biological signals can be used to evaluate stress levels. In this study, WESAD (Wearable Stress and Affect Detection) dataset is used, which is collected using wearable sensing devices such as wrist-worn. Further, multiple machine and deep learning models including support vector machine (SVM), Decision Tree (DT), k-nearest neighbors (k-NN), and long short-term memory (LSTM) are used for detecting human stress. The result shows that the LSTM achieved high model accuracy in comparison with other models in terms of precision (97.01%), recall (97.00%), F1-score (97.03%), and accuracy (98.00%). So, from the result, it is confirmed that LSTM modal may be used as stress prediction model.
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Malviya, L. et al. (2023). Mental Stress Level Detection Using LSTM for WESAD Dataset. In: Khanna, A., Polkowski, Z., Castillo, O. (eds) Proceedings of Data Analytics and Management . Lecture Notes in Networks and Systems, vol 572. Springer, Singapore. https://doi.org/10.1007/978-981-19-7615-5_22
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DOI: https://doi.org/10.1007/978-981-19-7615-5_22
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