Mohd Noh, Faridah Hanim and Wan Kamri, Wan Mohd Amin Khalili and Zakaria, Ahmad Hawari and Gimin, Nor Arina and Iqbal, Safa'at and Yaakub, Muhamad Faizal and Mohd Shah, Nor Shahida and Mashori, Sumaiya (2023) Development of physiological mouse for anxiety disorder identification system. In: 12th International Conference on Mechanical and Manufacturing Engineering 2022 , ICME 2022, 9 August 2022 through 10 August 2022, Virtual, Online.
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Development of physiological mouse for anxiety disorder identification system.pdf Restricted to Repository staff only Download (452kB) |
Abstract
Anxiety disorder has been known as one of mental disorder characterized by significant and uncontrollable feelings of anxiety and fear. Anxiety may cause physical and cognitive symptoms such as restlessness, irritability, easy fatigability, difficulty concentrating, increased heart rate, chest pain, abdominal pain, and many others. Motivated by current research that accompanies anxiety and stress with physical reactions such as increased heart rate, blood flow, dilation of the pupil and skin conductance, this work builds on the premise that real-time measurement of such reactions could indirectly recognize older adult anxiety while interacting with the system. For this research, an in-house computer mouse with embedded sensors circuit was constructed and simulated via Proteus environment to test the heart rate and skin conductance of the users. In this project a rules-based algorithm for distinguishing the anxiety disorder events have been developed. The detection is being processed based on the measured physiological data that being quantified via the embedded sensors in the mouse circuits. The result shows that the developed system able to detect mental health state of the user at accuracy of 0.87, which defines a good performance of the proposed system.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | Anxiety disorder, Physiological data, Heart rate, Skin conductance, Real-time detection |
| Divisions: | Faculty Of Electrical Technology And Engineering |
| Depositing User: | Anis Suraya Nordin |
| Date Deposited: | 17 Oct 2024 16:18 |
| Last Modified: | 17 Oct 2024 16:18 |
| URI: | http://eprints.utem.edu.my/id/eprint/28086 |
| Statistic Details: | View Download Statistic |
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