Stress Detection Using Machine Learning Algorithms

Stress management is becoming more and more crucial in today's fast-paced technological environment, particularly for IT professionals. Long working hours, strict deadlines and high expectations are common aspects of the work environment in the IT sector, and these can raise stress levels. Unmanaged stress has an adverse effect on professionals' health and well-being as well as their productivity and job happiness. A data set comprising of 2343 sample values taken from Kaggle is used for detecting the stress levels

Stress detection, Machine learning, Deep Neural Networks

1. R. Archana , B. M. Devaraju “Stress Detection using Machine Learning Algorithms” in International Journal of Research in Engineering, Science and Management, ISSN : 2581-5792, August 2020. [Google Scholar] [Crossref]

2. S. A Singh, P. K. Gupta, M. Rajeshwari, and T. Janumala,” Detection of Stress Using Biosensors,” Materials Today: Proceedings 5, 2018, pp. 21003–21010. [Google Scholar] [Crossref]

3. Fernandes, R. Helawar, R. Lokesh, T. Tari, and A. V. Shahapurkar, “Determination of stress using blood pressure and Galvanic skin response,” in Proc. IEEE Int. Conf. CommunicationandNetworkTechnologies,pp.165–168,201. [Google Scholar] [Crossref]

4. S. Elzeiny and M. Qaraqe, “Machine Learning Approaches to Automatic Stress Detection: A Review,” in Proc.Int. Conf. Computer Systems and Applications (AICCSA), 2018. [Google Scholar] [Crossref]

5. Reshma Radheshamjee, and Supriya Kinariwala “Detection and Analysis of Stress using Machine Learning Techniques” International Journal of Engineering and Advanced Technology, pp. 2249 8958, 2019. [Google Scholar] [Crossref]

6. Pramod Bobade, M.Vani “Stress Detection using Machine Learning and Deep Learning using Multimodal Physiological Data” in Second International Conference on Inventive Research in Computing Applications (ICIRCA), DOI: 10.1109/ICIRCA48905.2020.9183244, July 2020. [Google Scholar] [Crossref]

7. G. Poornima, B.Ashritha Reddy,M.Kiran Kumar, B.Hrithik Yadav, ”Body Stress Detection Using Machine Learning and IOT Technology” in International Research Journal of Modernization in Engineering Technology and Science, e- ISSN: 2582-5208, 2022. [Google Scholar] [Crossref]

8. Azhari, Ahmad and Leonel Hernandez," Brain waves feature classification by applying K-Means clustering using single-sensor EEG." International Journal of Advances in Intelligent Informatics 2.3, pp.167-173, Nov 2016. [Google Scholar] [Crossref]

9. Mehra, Anu, “Study of ECG signals based on gender and heartabnormalities." International Journal of Computational ComplexityandIntelligentAlgorithms,pp.277-291,Feb2020. [Google Scholar] [Crossref]

10. Barrios-Muriel, Jorge,"A simpleSSA-based de-noising technique to remove ECG interference in EMG signals." BiomedicalSignal Processing and Control, pp. 117-126, Sep 2016. [Google Scholar] [Crossref]

11. Elgendi M, Mohamed A,Ward R, “Efficient ECG compression and QRS detection for E-health applications,” Sci Rep, pp.459-471,Mar. 2017. [Google Scholar] [Crossref]

12. Kim, Hye-Geum, "Stress and heart rate variability: a meta- analysisandreviewoftheliterature,"Psychiatryinvestigation, pp.235-245,Mar 2018. [Google Scholar] [Crossref]

13. Behar,JoachimA,"A universal scaling relation for defining power spectral bands in mammalian heart rate variability analysis," Frontiers in physiology, pp. 1001-1005, Aug 2018. [Google Scholar] [Crossref]

14. Sbrollini, Agnese, et al, "Evaluation of the low-frequency components in surface electromyography," Annual InternationalConferenceoftheIEEEEngineeringinMedicine and Biology Society (EMBC), pp.3622-3626, Aug 2016. [Google Scholar] [Crossref]

15. Krasteva, Vessela, Irena Jekova, and Ramun Schmid, "Simulating arbitrary electrode reversals in standard 12-lead ECG," Sensors, pp. 20-29, Jul 2019. [Google Scholar] [Crossref]

16. Balouchestani, Mohammadreza, and Sridhar Krishnan, "Advanced K-means clustering algorithm for large ECG data sets based on a collaboration of compressed sensing theory and K-SVD approach," Signal, Image and Video Processing, pp. 113-120, Oct 2016. [Google Scholar] [Crossref]

17. Roopa, C.K., B.S. Harish,and SVAruna Kumar, "Classification of ECG Arrhythmia using symbolic dynamics through fuzzy clustering neural network, "Third International Workshop on Pattern Recognition International Society for Optics and Photonics, pp.108-114, Jul 2018. [Google Scholar] [Crossref]

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