Employing Machine Learning Algorithms to Detect Stress with a Specific Emphasis on Commuting Methods
Conference paper
Sharif, S., Theeng Tamang, M., Fu, C. and Elmedany, W. 2023. Employing Machine Learning Algorithms to Detect Stress with a Specific Emphasis on Commuting Methods. FiCloud 2023: The 10th International Conference on Future Internet of Things and Cloud. Marrkech, Morocco 14 - 16 Aug 2023 IEEE. https://doi.org/10.1109/FiCloud58648.2023.00067
Authors | Sharif, S., Theeng Tamang, M., Fu, C. and Elmedany, W. |
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Type | Conference paper |
Abstract | The regular commute for many individuals could significantly impact their general well-being. The daily commute to work can be linked to chronic stress, which is known to have negative implications on mental health, as well as increased blood pressure, heightened heart rate, and high fatigue. The primary objective of this study is to examine the physiological effects of commuting using machine learning techniques, with a specific emphasis on analysing the impact of different transportation methods. Healthy individuals were recruited to collect various biological signals, such as blood pressure (BP), heart rate, and electroencephalogram (EEG) data. By leveraging multiple machine learning techniques, we examined the effects of different commuting modes, whether short or long. Our findings revealed an increase in objective bio signals following the commute. Furthermore, when comparing stress levels between different commute modes, we observed that driving is more stressful than other modes, like public transport. We obtained highly encouraging outcomes by implementing the support vector machine (SVM) algorithm, which exhibited an impressive accuracy of 93.2%. In comparison, the K-nearest neighbour (KNN) and Naïve Bayes algorithms yielded good accuracy of 87.9%. Similarly, by utilising the PANAS questionnaire, we observed that the positive affect levels were greater before the commute. This suggests that participants demonstrated a higher degree of positivity and enthusiasm towards their work prior to boarding on their commute. |
Year | 2023 |
Conference | FiCloud 2023: The 10th International Conference on Future Internet of Things and Cloud |
Publisher | IEEE |
Accepted author manuscript | License File Access Level Anyone |
Publication dates | |
Online | 29 Jan 2024 |
Publication process dates | |
Accepted | 20 May 2023 |
Deposited | 19 Jun 2023 |
Journal citation | pp. 416-421 |
Book title | Proceedings: 10th International Conference on Future Internet of Things and Cloud (FiCloud 2023) |
Book editor | Awan, I. |
Younas, M. | |
Aleksy, M. | |
ISBN | 9798350316353 |
9798350316360 | |
Digital Object Identifier (DOI) | https://doi.org/10.1109/FiCloud58648.2023.00067 |
Web address (URL) of conference proceedings | https://ieeexplore.ieee.org/xpl/conhome/10410629/proceeding |
External resource | MobiApps 2023: International Workshop on Mobile Applications |
Copyright holder | © 2023, IEEE |
Copyright information | Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Additional information | This paper was presented in MobiApps 2023: International Workshop on Mobile Applications, which was co-located within The 10th International Conference on Future Internet of Things and Cloud (FiCloud 2023) and the 19th International Conference on Mobile Web and Intelligent Information Systems (MobiWIS 2023). |
https://repository.uel.ac.uk/item/8w2zx
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Accepted author manuscript
MobiApps'23 - Employing Machine Learning Algorithms to Detect Stress with a Specific Emphasis on Commuting Methods.pdf | ||
License: All rights reserved | ||
File access level: Anyone |
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