Stress detection and management using machine learning : A comprehensive approach
*Ruchi SharmaCorresponding authorruchi.sharma@bharatividyapeeth.eduDepartment of Electronics and Communication EngineeringBharati Vidyapeeth’s College of EngineeringPaschim Vihar, New Delhi, 110063, IndiaView full profile → , Mihikamihika.ece@bharatividyapeeth.eduDepartment of Electronics and Communication EngineeringBharati Vidyapeeth’s College of EngineeringPaschim Vihar, New Delhi, 110063, IndiaView full profile → , Annu Dabasannu.dabas@bharatividyapeeth.eduDepartment of Electronics and Communication EngineeringBharati Vidyapeeth’s College of EngineeringPaschim Vihar, New Delhi, 110063, IndiaView full profile → , Suman Yadavsuman.yadav@bharatividyapeeth.eduDepartment of Electronics and Communication EngineeringBharati Vidyapeeth’s College of EngineeringPaschim Vihar, New Delhi, 110063, IndiaView full profile → , Rubeena Vohrarubeena.vohra@bharatividyapeeth.eduDepartment of Electronics and Communication EngineeringBharati Vidyapeeth’s College of EngineeringPaschim Vihar, New Delhi, 110063, IndiaView full profile → , Saji M. Antonysaji.antony@bharatividyapeeth.eduDepartment of Electronics and Communication EngineeringBharati Vidyapeeth’s College of EngineeringPaschim Vihar, New Delhi, 110063, IndiaView full profile → , Gaurav Mitragaurav.mitra@bharatividyapeeth.eduDepartment of Electronics and Communication EngineeringBharati Vidyapeeth’s College of EngineeringPaschim Vihar, New Delhi, 110063, IndiaView full profile → , Arvind Rehaliaarvind.rehalia@bharatividyapeeth.eduDepartrment of Information and Technology EngineeringBharati Vidyapeeth’s College of EngineeringPaschim Vihar, New Delhi, 110063, IndiaView full profile → , Vijay Kumarvijay.kumar@bharatividyapeeth.eduDepartrment of Computer Science EngineeringBharati Vidyapeeth’s College of EngineeringPaschim Vihar, New Delhi, 110063, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 13 Aug 2024
- Published Online:
- 19 Feb 2025
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-1861
- Pages:
- 167–177
Abstract
Keywords
Subject Classifications
References
[1] Y. Kim, S. Park, and J. Lee, “Real-time stress level detection using a wearable device and machine learning algorithms,” IEEE Transactions on Mobile Computing, vol. 16, no. 12, pp. 1-10 (Dec. 2017).
[2] G. Chen, L. Xu, and M. Zhang, “Stress prediction using self-reported data and machine learning,” Journal of Medical Systems, vol. 42, no. 5, pp. 78-84 (May 2018).
[3] J. Park, H. Choi, and S. Lee, “Mobile application for personalized stress management using machine learning,” Journal of Healthcare Informatics Research, vol. 5, no. 2, pp. 121-130 (2019).
[4] T. Nguyen, H. Lim, and K. Park, “Wearable stress monitoring system with machine learning-based prediction,” Sensors and Actuators A: Physical, vol. 229, pp. 194-202 (Nov. 2015).
[5] X. Yao, R. Liu, and J. Wu, “A review on machine learning methods for stress prediction: Application in wearable sensors,” IEEE Access, vol. 6, pp. 60883-60891 (Sept. 2018).
[6] J. Bollen, Y. Mao, and A. Pepe, “Mobile phone data analysis for stress prediction using machine learning,” Computational Intelligence, vol. 32, no. 3, pp. 342-357 (June 2016).
[7] A. Sharma, P. Gupta, and R. Mishra, “Comparison of machine learning techniques for stress prediction: Self-reported vs physiological data,” IEEE Transactions on Biomedical Engineering, vol. 65, no. 7, pp. 1567-1574 (July 2017).
[8] S. S. Panicker and P. Gayathri, “A survey of machine learning techniques in physiology-based mental stress detection systems,” Biocybernetics and Biomedical Engineering, vol. 39, pp. 444-469 (2019).
[9] K. Peternel, M. Pogacnik, R. Tavcar, and A. Kos, “A presence-based context-aware chronic stress recognition system,” Sensors, vol. 12, pp. 15888-15906 (2012).
[10] M. Bickford, “Stress in the workplace: A general overview of the causes, the effects, and the solutions,” Canadian Mental Health Association, pp. 1-3 (2005).
[11] K. E. Wellen and G. S. Hotamisligil, “Inflammation, stress, and diabetes,” Journal of Clinical Investigation, vol. 115, pp. 1111- 1119 (2005).
[12] C. T. Li, J. Cao, and T. M. Li, “Eustress or distress: An empirical study of perceived stress in everyday college life,” in Proc. ACM Int. Joint Conf. Pervasive Ubiquitous Computing, pp. 1209-1217 (2016).
[13] W. Chen, S. Zheng, and X. Sun, “Introducing mdpsd, a multimodal dataset for psychological stress detection,” in Big Data: 8th CCF Conf., Chongqing, China, pp. 59-68 (2020).
[14] C. Kirschbaum, K. M. Pirke, and D. H. Hellhammer, “The ‘trier social stress test’–a tool for investigating psychobiological stress responses in a laboratory setting,” Neuropsychobiology, vol. 28, pp. 76-81 (1993).
[15] E. Ferrer, Henry Scaffman and J. Johsponsk, “Playing music to relieve stress in a college classroom environment,” College Student Journal, vol. 48, pp. 481-494 (2014).
[16] S. Vos and M. R. Azghadi, “Generalizable machine learning for stress monitoring from wearable devices: A systematic literature review,” IEEE Access, vol. 9, pp. 111320- 111339 (2021).
[17] M. A. Bahman, S. Memarzadeh-Tehran, and N. K. Fernandez, “Stress detection using wearable sensors and deep learning techniques,” IEEE Sensors Journal, vol. 21, no. 22, pp. 25356- 25365 (Nov. 2021).
