An innovative random-forest-based model to assess the health impacts of regular commuting using non-invasive wearable sensors
Open accessAbstract
Regular commutes to work can cause chronic stress, which in turn can cause a physical and emotional reaction. The recognition of mental stress in its earliest stages is very necessary for effective clinical treatment. This study investigated the impact of commuting on human health based on qualitative and quantitative measures. The quantitative measures included electroencephalography (EEG) and blood pressure (BP), as well as weather temperature, while qualitative measures were established from the PANAS questionnaire, and included age, height, medication, alcohol status, weight, and smoking status. This study recruited 45 (n) healthy adults, including 18 female and 27 male participants. The modes of commute were bus (n = 8), driving (n = 6), cycling (n = 7), train (n = 9), tube (n = 13), and both bus and train (n = 2). The participants wore non-invasive wearable biosensor technology to measure EEG and blood pressure during their morning commute for 5 days in a row. A correlation analysis was applied to find the significant features associated with stress, as measured by a reduction in positive ratings in the PANAS. This study created a prediction model using random forest, support vector machine, naive Bayes, and K-nearest neighbor. The research results show that blood pressure and EEG beta waves were significantly increased, and the positive PANAS rating decreased from 34.73 to 28.60. The experiments revealed that measured systolic blood pressure was higher post commute than before the commute. For EEG waves, the model shows that the EEG beta low power exceeded alpha low power after the commute. Having a fusion of several modified decision trees within the random forest helped increase the performance of the developed model remarkably. Significant promising results were achieved using random forest with an accuracy of 91%, while K-nearest neighbor, support vector machine, and naive Bayes performed with an accuracy of 80%, 80%, and 73%, respectively.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Early wearable AI models primarily used traditional machine-learning techniques suitable for time-series analysis, such as support vector machine, logistic regression, and random forest.
“Early wearable AI models primarily used traditional machine-learning techniques suitable for time-series analysis, such as support vector machine, logistic regression, and random forest (Table 1).19”
From Unlocking Tomorrow’s Health Care: Expanding the Clinical Scope of Wearables by Applying Artificial Intelligence (2024) · cites this paper as [19]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Development and validation of brief measures of positive and negative affect: The PANAS scales.
Watson, D., Clark, L. A., & Tellegen, A. (1988) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.54.6.1063 · Cited by 32,433 · see citing works
Systolic versus diastolic blood pressure and risk of coronary heart disease
Kannel, W. B., Gordon, T., & Schwartz, M. J. (1971) · The American Journal of Cardiology · doi:10.1016/0002-9149(71)90428-0 · Cited by 925 · see citing works
Receiver operating characteristic (ROC) curve for medical researchers
Kumar, R., & Indrayan, A. (2011) · Indian Pediatrics · doi:10.1007/s13312-011-0055-4 · Cited by 872 · see citing works
Effect of systolic and diastolic blood pressure on cardiovascular outcomes
Flint, A. C., Conell, C., Ren, X., Banki, N. M., Chan, S. L., Rao, V. A., Melles, R. B., & Bhatt, D. L. (2019) · New England Journal of Medicine · doi:10.1056/nejmoa1803180 · Cited by 684 · see citing works
Affective Appraisals of the Daily Commute
Gatersleben, B., & Uzzell, D. L. (2007) · Environment and Behavior · doi:10.1177/0013916506294032 · Cited by 478 · see citing works
Chatterjee, K., Chng, S., Clark, B., Davis, A., Vos, J. D., Ettema, D., Handy, S., Martin, A., & Reardon, L. (2019) · Transport Reviews · doi:10.1080/01441647.2019.1649317 · Cited by 453 · see citing works
Stress Detection with Machine Learning and Deep Learning using Multimodal Physiological Data
Bobade, P., & Vani, M. P. (2020) · International Conference on Inventive Research in Computing Applications (ICIRCA) · doi:10.1109/icirca48905.2020.9183244 · Cited by 302 · see citing works
Mental Stress Detection in University Students using Machine Learning Algorithms
Ahuja, R., & Banga, A. (2019) · Procedia Computer Science · doi:10.1016/j.procs.2019.05.007 · Cited by 234 · see citing works
A Study on the Relationships of Classifier Performance Metrics
Seliya, N., Khoshgoftaar, T. M., & Hulse, J. V. (2009) · Proceedings - International Conference on Tools with Artificial Intelligence, TAI · doi:10.1109/ictai.2009.25 · Cited by 225 · see citing works
Díaz-García, A., González-Robles, A., Mor, S., Mira, A., Quero, S., García-Palacios, A., Baños, R. M., & Botella, C. (2020) · BMC Psychiatry · doi:10.1186/s12888-020-2472-1 · Cited by 207 · see citing works
Hou, X., Liu, Y., Sourina, O., Tan, Y., Wang, L., & Müeller-Wittig, W. (2015) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2015.540 · Cited by 199 · see citing works
A Multi-Column CNN Model for Emotion Recognition from EEG Signals
Yang, H., Han, J., & Min, K. (2019) · Sensors · doi:10.3390/s19214736 · Cited by 178 · see citing works
Stress Detection in Working People
Sriramprakash, S., Prasanna, V. D., & Murthy, O. V. R. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.09.090 · Cited by 174 · see citing works
Comparing stress of car and train commuters
Wener, R. E., & Evans, G. W. (2010) · Transportation Research Part F Traffic Psychology and Behaviour · doi:10.1016/j.trf.2010.11.008 · Cited by 160 · see citing works
Stress Analysis Based on Simultaneous Heart Rate Variability and EEG Monitoring
Attar, E. T., Balasubramanian, V., Subasi, E., & Kaya, M. (2021) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2021.3106803 · Cited by 133 · see citing works
Hämmig, O., Gutzwiller, F. F., & Bauer, G. F. (2009) · BMC Public Health · doi:10.1186/1471-2458-9-435 · Cited by 132 · see citing works
Anger, Emotion, and Arrhythmias: From Brain to Heart
Taggart, P. I., Boyett, M. R., Logantha, S. J. R. J., & Lambiase, P. D. (2011) · Frontiers in Physiology · doi:10.3389/fphys.2011.00067 · Cited by 125 · see citing works
Predicting Increased Blood Pressure Using Machine Learning
Golino, H. F., Amaral, L. S. D. B., Duarte, S. F. P., Gomes, C. M. A., Soares, T. D. J., dos Reis, L. A., & Santos, J. (2014) · Journal of Obesity · doi:10.1155/2014/637635 · Cited by 118 · see citing works
Lee, D.-H., Jeong, J.-H., Kim, K., Yu, B.-W., & Lee, S. (2020) · IEEE Access · doi:10.1109/access.2020.3006907 · Cited by 116 · see citing works
Commuting — a further stress factor for working people: evidence from the European Community
Costal, G., Pickup, L., & Martino, V. D. (1988) · International Archives of Occupational and Environmental Health · doi:10.1007/bf00405674 · Cited by 115 · see citing works
Machine learning-based signal processing using physiological signals for stress detection
Ghaderi, A., Frounchi, J., & Farnam, A. (2015) · Iranian Conference on Biomedical Engineering · doi:10.1109/icbme.2015.7404123 · Cited by 107 · see citing works
Travel Time and Subjective Well-Being
Choi, J. S., Coughlin, J. F., & D’Ambrosio, L. A. (2013) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2357-12 · Cited by 105 · see citing works
A physiological signal-based method for early mental-stress detection
Xia, L., Malik, A., & Subhani, A. (2018) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2018.06.004 · Cited by 86 · see citing works
Ventricular Tachyarrhythmia Associated With Psychological Stress
Brodsky, M. A. (1987) · JAMA · doi:10.1001/jama.1987.03390150080039 · Cited by 80 · see citing works
Augmenting Weighted Average with Confusion Matrix to Enhance Classification Accuracy
Patro, V. M., & Patra, M. R. (2014) · Transactions on Machine Learning and Artificial Intelligence · doi:10.14738/tmlai.24.328 · Cited by 80 · see citing works
Design of a Biosignal Based Stress Detection System Using Machine Learning Techniques
Rizwan, M. F., Farhad, R., Mashuk, F., et al. (2019) · International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST) · doi:10.1109/icrest.2019.8644259 · Cited by 71 · see citing works
Time Spent Commuting to Work and Mental Health: Evidence From 13 Waves of an Australian Cohort Study
Milner, A. J., Badland, H. M., Kavanagh, A. M., & LaMontagne, A. D. (2017) · American Journal of Epidemiology · doi:10.1093/aje/kww243 · Cited by 69 · see citing works
Cho, H.-M., Park, H. S., Dong, S.-Y., & Youn, I. (2019) · Sensors · doi:10.3390/s19204408 · Cited by 53 · see citing works
Ahmed, T., Qassem, M., & Kyriacou, P. A. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103591 · Cited by 44 · see citing works
Personality trait prediction by machine learning using physiological data and driving behavior
Evin, M. A., Hidalgo-Muñoz, A. R., Béquet, A. J., Moreau, F., Tattegrain, H., Berthelon, C., Fort, A., & Jallais, C. (2022) · Machine Learning with Applications · doi:10.1016/j.mlwa.2022.100353 · Cited by 44 · see citing works
Miranda-Correa, J. A., & Patras, I. (2018) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2018.00060 · Cited by 37 · see citing works
Evaluation of Artificial Neural Networks in Prediction of Essential Hypertension
Samant, R., & Rao, S. (2013) · International Journal of Computer Applications · doi:10.5120/14067-2331 · Cited by 34 · see citing works
AI-enabled remote and objective quantification of stress at scale
Al-Jebrni, A. H., Chwyl, B., Wang, X. Y., Wong, A., & Saab, B. J. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.101929 · Cited by 21 · see citing works
Offline LabView-based EEG Signals Analysis for Human Stress Monitoring
Sulaiman, N., Ying, B. S., Mustafa, M., et al. (2018) · IEEE Control and System Graduate Research Colloquium (ICSGRC) · doi:10.1109/icsgrc.2018.8657606 · Cited by 17 · see citing works
Empirical Validation of cross-version and 10-fold cross-validation for Defect Prediction
Malhotra, R., & Meena, S. (2021) · International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc51422.2021.9533030 · Cited by 15 · see citing works
Performance Evaluation of an ANN FF Classifier of Raw EEG Data using ROC Analysis
Sovierzoski, M. A., de Azevedo, F. M., & Argoud, F. I. M. (2008) · International Conference on Biomedical Engineering and Informatics (BMEI) · doi:10.1109/bmei.2008.220 · Cited by 8 · see citing works
Blood Pressure Modeling using Statistical and Computational Intelligence Approaches
Bhaduri, A., Bhaduri, A., Bhaduri, A., & Mohapatra, P. K. (2009) · IEEE International Advance Computing Conference · doi:10.1109/iadcc.2009.4809156 · Cited by 7 · see citing works
F-score Based EEG Channel Selection Methods for Emotion Recognition
Zhao, C., Li, C., Chao, J., Wang, T., Lei, C., Liu, J., & Peng, H. (2021) · IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom49281.2021.9399019 · Cited by 5 · see citing works
Artificial Neural Network Based Stress Level Detection System using Physiological Signals
Pascual, R. K. C., Serrano, J. P. D., Soltez, J. M. A., Castillo, J. C. D., Torres, J. L., & Cruz, F. R. G. (2018) · IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) · doi:10.1109/hnicem.2018.8666339 · Cited by 4 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [29]Daniel W.W. Cross C.L. Biostatistics: A Foundation for Analysis in the Health Sciences Wiley Hoboken, NJ, USA 2018
Cited by
Sorted by citations
Marvasti, T. B., Gao, Y., Murray, K. R., Hershman, S., McIntosh, C., & Moayedi, Y. (2024) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2024.07.009 · Cited by 35 · see citing works
- Authors
- Sharif, M. S., Raj Theeng Tamang, M., Fu, C. H. Y., Baker, A., Alzahrani, A. I., & Alalwan, N.
- Affiliations
- University of East London (GB)
- Intelligent Technologies Research Group, ACE, UEL, University Way, London E16 2RD, UK
- King Saud University (SA)
- Corresponding author
- Sharif, Mhd Saeed
- ORCID
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2023-03-20
- Volume
- 23
- Issue
- 6
- Pages
- 3274
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC10055922
- OpenAlex
- W4327952382
- Cited by
- 22
- References
- 39
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 6 · 2023: 3
- FWCI
- 5.69
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- KSU (RSP2023R157)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- commuting, chronic stress, mental stress detection, wearable biosensors, EEG, blood pressure, PANAS, EEG beta waves, random forest, stress prediction model, transport modes
- MeSH terms
- Wearable Electronic Devices, Adult, Bayes Theorem, Electroencephalography, Humans, Surveys and Questionnaires, Transportation, Support Vector Machine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
