International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Promoting Remote Employee Well-being: Role of Emotion Detection, Social Media Analysis, Mental Health Monitoring, and Performance Tracking

Abstract

This research focuses on detecting and monitoring human emotions in remote employees to enhance mental well-being and work efficiency. Emotions are seen using cameras and heart rate measurements, comparing the two for accuracy. Sequential deep-learning models and sentiment analysis are employed to analyze social media behavior, with the goal of identifying and understanding the emotions expressed. Music recommendations are made based on the identified emotions. The study also monitors the mental health of remote employees by collecting feedback, predicting stress levels, and recommending therapies based on sleep data and emotional inputs. Additionally, employee performance is tracked by monitoring task completion and web activity, providing insights into work hours and productivity. This research aims to improve remote employees' mental health and work outcomes through emotion detection, social media analysis, mental health monitoring, therapy recommendation, and performance tracking.

Country : Sri Lanka

1 Kushan Dimantha De Silva2 Hivindu Punsith3 H.D. Nethmi Prabodhika Damayanthi4 Ramith Perera5 Sanjeevi Chandrasiri6 Hansi De Silva

  1. Department of Computer Science & Software Engineering, Faculty of Computing, Sri Lanka Institute of Information Technology, New Kandy Road, Malabe, Sri Lanka
  2. Department of Computer Science & Software Engineering, Faculty of Computing, Sri Lanka Institute of Information Technology, New Kandy Road, Malabe, Sri Lanka
  3. Department of Computer Science & Software Engineering, Faculty of Computing, Sri Lanka Institute of Information Technology, New Kandy Road, Malabe, Sri Lanka
  4. Department of Computer Science & Software Engineering, Faculty of Computing, Sri Lanka Institute of Information Technology, New Kandy Road, Malabe, Sri Lanka
  5. Department of Computer Science & Software Engineering, Faculty of Computing, Sri Lanka Institute of Information Technology, New Kandy Road, Malabe, Sri Lanka
  6. Department of Computer Science & Software Engineering, Faculty of Computing, Sri Lanka Institute of Information Technology, New Kandy Road, Malabe, Sri Lanka

IRJIET, Volume 7, Issue 10, October 2023 pp. 42-48

doi.org/10.47001/IRJIET/2023.710006

Full Paper
Download

References

  1. K. Kanakia, L. Moharkar, S. Patil, and S. Sabnis, “Emotion & Heartbeat Detection using Image Processing,” vol. 9, no. 3, 2018.
  2. A.Dzedzickis, A. Kaklauskas, and V. Bucinskas, “Human Emotion Recognition: Review of Sensors and Methods,” Sensors, vol. 20, no. 3, p. 592, Jan. 2020, doi: 10.3390/s20030592.
  3. L. Shu et al., “Wearable Emotion Recognition Using Heart Rate Data from a Smart Bracelet,” Sensors, vol. 20, no. 3, p. 718, Jan. 2020, doi: 10.3390/s20030718.
  4. H. P. H. Wathsara, M. L. P. D. Dhananjana, D. A. Nanayakkara, B. B. K. R. Dharmarathne, L. H. S. Chandrasiri, and J. K. Joseph, “Employee Work Motivation Detection using Image and Audio Processing,” IJCA, vol. 175, no. 31, pp. 34–40, Nov. 2020, doi: 10.5120/ijca2020920862.
  5. T. R. S. De Silva, K. Y. Dayananda, R. C. Galagama Arachchi, M. K. S. B. Amerasekara, S. Silva, and N. Gamage, “Solution to Measure Employee Productivity with Employee Emotion Detection,” in 2022 4th International Conference on Advancements in Computing (ICAC), Colombo, Sri Lanka: IEEE, Dec. 2022, pp. 210–215. doi: 10.1109/ICAC57685.2022.10025132.
  6. A.Chowanda, J. Moniaga, J. C. Bahagiono, and J. Sentosa Chandra, “Machine Learning Face Recognition Model for Employee Tracking and Attendance System,” in 2022 International Conference on Information Management and Technology (ICIMTech), Semarang, Indonesia: IEEE, Aug. 2022, pp. 297–301. doi: 10.1109/ICIMTech55957.2022.9915078.
  7. N. Sharma and P. Hosein, “A Comparison of Data-Driven and Traditional Approaches to Employee Performance Assessment,” in 2020 International Conference on Intelligent Data Science Technologies and Applications (IDSTA), Valencia, Spain: IEEE, Oct. 2020, pp. 34–41. doi: 10.1109/IDSTA50958.2020.9264033.
  8. “How To Recognize And Avoid Workplace Stress,” Zippia, Feb. 26, 2023. https://www.zippia.com/advice/remote-work-burnout-statistics/ (accessed Jul. 04, 2023).
  9. T. Fritz and J. M. Cotilla Conceição, “Mental Health of Remote Working Employees in Europe,” Mar. 2021.
  10. F. Y. Huang, H. Chung, K. Kroenke, K. L. Delucchi, and R. L. Spitzer, “Using the Patient Health Questionnaire-9 to Measure Depression among Racially and Ethnically Diverse Primary Care Patients,” Journal of General Internal Medicine, vol. 21, no. 6, pp. 547–552, 2006, doi: 10.1111/j.1525-1497.2006.00409.x.
  11. D. Nori, “Hero: Automated Detection System for Prescription Stimulant Overdose via AI-Based Emotion Inference, Metabolite Detection, and Biometric Measurement,” Open Journal of Applied Sciences, vol. 10, no. 12, Art. no. 12, Dec. 2020, doi: 10.4236/ojapps.2020.1012056.
  12. M. Roshanaei, R. Han, and S. Mishra, “Features for mood prediction in social media,” in Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, in ASONAM ’15. New York, NY, USA: Association for Computing Machinery, Aug. 2015, pp. 1580–1581. doi: 10.1145/2808797.2809342.
  13. X. Chen and T. Y. Tang, “Combining Content and Sentiment Analysis on Lyrics for a Lightweight Emotion-Aware Chinese Song Recommendation System,” in Proceedings of the 2018 10th International Conference on Machine Learning and Computing, in ICMLC 2018. New York, NY, USA: Association for Computing Machinery, Feb. 2018, pp. 85–89. doi: 10.1145/3195106.3195148.
  14. K. S. Nathan, M. Arun, and M. S. Kannan, “EMOSIC — An emotion based music player for Android,” in 2017 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Dec. 2017, pp. 371–276. doi: 10.1109/ISSPIT.2017.8388671.

Copyright @ 2026-2017 IRJIET. All Right Reserved.

Developed by Byzero Technologies