Volume 15, Issue 3 (12-2018)                   JSDP 2018, 15(3): 59-74 | Back to browse issues page

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Golgouneh A, Tarvirdizadeh B. Development of a Mechatronics System to Real-Time Stress Detection Based on Physiological Signals. JSDP. 2018; 15 (3) :59-74
URL: http://jsdp.rcisp.ac.ir/article-1-611-en.html
Abstract:   (1258 Views)

Stress has affected human’s lives in many areas, today. Stress can adversely affect human’s health to such a degree as to either cause death or indicate a major contributor to death. Therefore, in recent years, some researchers have focused to developing systems to detect stress and then presenting viable solutions to manage this issue.
Generally, stress can be identified through three different methods including (1) Psychological Evaluation, (2) Behavioral Responses and finally (3) Physiological Signals. Physiological signals are internal signs of functioning the body, and therefore nowadays are commonly used in various medical and non-medical applications. Since these signals are correlated with the stress, they have been commonly used in detection of the stress in humans. Photoplethysmography (PPG) and Galvanic Skin Response (GSR) are two of the most common signals which have been widely used in many stress related studies. PPG is a noninvasive method to measure the blood volume changes in blood vessels and GSR refers to changes in sweat gland activity that are reflective of the intensity of human emotional state.
Design and fabrication of a real-time handheld system in order to detect and display the stress level is the main aim of this paper. The fabricated stress monitoring device is completely compatible with both wired and wireless sensor devices. The GSR and PPG signals are used in the developed system. The mentioned signals are acquired using appropriate sensors and are displayed to the user after initial signal processing operation. The main processor of the developed system is ARM-cortex A8 and its graphical user interface (GUI) is based on C++ programming language. Artificial Neural Networks such as MLP and Adaptive Neuro-Fuzzy Inference System (ANFIS) are utilized to modeling and estimation of the stress index. The results show that ANFIS model have a good accuracy with a coefficient of determination values of 0.9291 and average relative error of 0.007.

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Type of Study: Research | Subject: Paper
Received: 2017/12/20 | Accepted: 2018/07/25 | Published: 2018/12/19 | ePublished: 2018/12/19

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