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Classification of Mental Stress from Wearable Physiological Sensors Using Image-Encoding-Based Deep Neural Network by Sayandeep Ghosh; SeongKi Kim; Muhammad Fazal Ijaz; Pawan Kumar Singh; Mufti Mahmud is a Engineering article available to read on EtoBox.

What is Classification of Mental Stress from Wearable Physiological Sensors Using Image-Encoding-Based Deep Neural Network about?

The human body is designed to experience stress and react to it, and experiencing challenges causes our body to produce physical and mental responses and also helps our body to adjust to new situations. However, stress becomes a problem when it continues to remain without a period of relaxation or relief. When a person has long-term stress, continued activation of the stress response causes wear and tear on the body. Chronic stress results in cancer, cardiovascular disease, depression, and diabetes, and thus is deeply detrimental to our health. Previous researchers have performed a lot of work regarding mental stress, using mainly machine-learning-based approaches. However, most of the methods have used raw, unprocessed data, which cause more errors and thereby affect the overall model performance. Moreover, corrupt data values are very common, especially for wearable sensor datasets, which may also lead to poor performance in this regard. This paper introduces a deep-learning-based method for mental stress detection by encoding time series raw data into Gramian Angular Field images, which results in promising accuracy while detecting the stress levels of an individual. The experim

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Author

Sayandeep Ghosh; SeongKi Kim; Muhammad Fazal Ijaz; Pawan Kumar Singh; Mufti Mahmud

Publisher

MDPI AG

Published

2022

Language

EN

Field

Engineering (Physical Sciences)