Development of a Mechatronics System to Real-Time Stress Detection Based on Physiological Signals

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Golgouneh Alireza; TARVIRDIZADEH BAHRAM

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Journal: SIGNAL AND DATA PROCESSING Year:2018 | Volume:15 | Issue:3 (37) Page(s): 59-74

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Title

Development of a Mechatronics System to Real-Time Stress Detection Based on Physiological Signals

Author(s)

Golgouneh Alireza | TARVIRDIZADEH BAHRAM | Issue Writer Certificate

Keywords

StressQ1

Physiological signalsQ1

GSRQ2

PPGQ2

Artificial Neural NetworksQ1

ANFISQ1

Abstract

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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APA: Copy

Golgouneh, Alireza, & TARVIRDIZADEH, BAHRAM. (2018). Development of a Mechatronics System to Real-Time Stress Detection Based on Physiological Signals. SIGNAL AND DATA PROCESSING, 15(3 (37) ), 59-74. SID. https://sid.ir/paper/160857/en

Vancouver: Copy

Golgouneh Alireza, TARVIRDIZADEH BAHRAM. Development of a Mechatronics System to Real-Time Stress Detection Based on Physiological Signals. SIGNAL AND DATA PROCESSING[Internet]. 2018;15(3 (37) ):59-74. Available from: https://sid.ir/paper/160857/en

IEEE: Copy

Alireza Golgouneh, and BAHRAM TARVIRDIZADEH, “Development of a Mechatronics System to Real-Time Stress Detection Based on Physiological Signals,” SIGNAL AND DATA PROCESSING, vol. 15, no. 3 (37) , pp. 59–74, 2018, [Online]. Available: https://sid.ir/paper/160857/en

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