AI-BASED EMPLOYEE MONITORING SYSTEM
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Dept. of CSE
Sri Revana Siddeshwara Institute of Technology, Chikkajala, Banglore 562157
Sri Revana Siddeshwara Institute of Technology, Chikkajala, Banglore 562157
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Abstract
This paper proposes a local monitoring system for employees. It consists of collecting keyboard and mouse activity, monitoring running applications. Additionally, it involves the use of a web cam for detecting the presence of a face, saving the data to a backend, analysing data using a dashboard and machine learning classification. The information regarding keyboard and mouse events are collected using Python agent. We don't log the specific button pressed. Also, we log information about what is the current active window title, estimate how much time they spent on the approved application. And we check for the presence of at least one face. Every ~90 seconds we upload the observations of monitoring to FastAPI backend. Backend stores the data into an SQLite database using SQLAlchemy. React dashboard is used for giving the overall information about the employee data, score trend, face presence statistics, application details, filters, CSV exports. We try to send an email alert using smtp after ~60 seconds of not detecting the face. Data is analysed for a weighted score, and also Random forest classification is performed. The given dataset contains 1500 labelled (3classes) with 500 per class. an 80% -20% stratified split which resulted in a 91.33% accuracy for test set. This means that the model is consistent with the provided labels but it doesn't necessarily mean it can measure employee productivity. The system was built to enable contextual analysis.
Keywords
Employee monitoring Keyboard and mouse activity Face detection Random Forest Productivity classification.
Article Information
PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 11
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Nov 07 2026
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Gowda, P., N, U. B., HN, A., D, J., Kumar, S. & S, S. (2026). AI-Based Employee Monitoring System. International Journal of Science, Strategic Management and Technology, 02(11), 1-9. https://doi.org/10.55041/ijsmt.v2i11.012
Gowda, Pradeep, et al.. "AI-Based Employee Monitoring System." International Journal of Science, Strategic Management and Technology, vol. 02, no. 11, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijsmt.v2i11.012.
Gowda, Pradeep,Ushashree N,Adhvi HN,Jeevitha D,Sai Kumar, and Srinidhi S. "AI-Based Employee Monitoring System." International Journal of Science, Strategic Management and Technology 02, no. 11 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijsmt.v2i11.012.
References
[1] R. Sharma and P. Verma, “AI Based Workplace Monitoring System,” International Journal of Computer Applications, 2023. Source: https://www.blue-bird.ai/
[2] A. Kumar and M. Reddy, “Smart Productivity Tracking Using Computer Activity,” IEEE International Conference on Smart Computing, 2022. Source: https://www.mdpi.com/resolver?pii=s22197363
[3] S. Patil and N. Joshi, “Employee Attendance and Monitoring System,” Journal of Artificial Intelligence Research, 2021. Source: https://pmc.ncbi.nlm.nih.gov/articles/PMC11855051/
[4] K. Srinivas and V. Rao, “Real-Time Office Surveillance Using Computer Vision,” International Journal of Engineering Research and Technology, 2022. Source: https://www.researchgate.net/publication/382953872_A_Comprehensive_Survey_on_Smart_Wearables_Real-time_Health_and_Workplace_Monitoring_Alerting_and_Analysis_in_the_Industrial_Context
[5] L. Fernandes and T. Joseph, “Employee Productivity Dashboard System,” Springer International Conference on Data Analytics, 2023. Source: https://www.researchgate.net/publication/390661243wearableiotdeviceswithaiforoccupationalhealthreal-time_worker_monitoring_and_safety_analytics
[6] M. Awada, B. Becerik-Gerber, G. Lucas, and R. C. Roll, “Forecasting the Performance of Office Employees: A Machine Learning Perspective Involving Biological, Behavioral, and Psychological Parameters,” Sensors, 2023. Source: https://www.sciencedirect.com/science/article/pii/S187705092502633X
[7] “Computer Vision-based Employee Monitoring System: Face and Action Identification,” Procedia Computer Science, 2025. Source: https://www.sciencedirect.com/science/article/pii/S187705092502633X
[8] Y. H. Ko and I. G. Baek, “The Effect of Computer Monitoring on Employees’ Productivity in Telecommuting Arrangements,” Management Science, 2025. Source: https://pubsonline.informs.org/doi/10.1287/mnsc.2022.00588
[9] T. Mettler, “The Connected Workplace: Characteristics and Social Consequences of Work Surveillance in the Age of Datification, Sensorization and Artificial Intelligence,” Journal of Information Technology, 2024.: https://journals.sagepub.com/doi/10.1177/02683962231202535
[10] D. M. Ravid et al., “A Meta-Analysis of the Effects of Electronic Performance Monitoring on Work Outcomes,” Personnel Psychology, 2023. Source: https://onlinelibrary.wiley.com/doi/abs/10.1111/peps.12514
[2] A. Kumar and M. Reddy, “Smart Productivity Tracking Using Computer Activity,” IEEE International Conference on Smart Computing, 2022. Source: https://www.mdpi.com/resolver?pii=s22197363
[3] S. Patil and N. Joshi, “Employee Attendance and Monitoring System,” Journal of Artificial Intelligence Research, 2021. Source: https://pmc.ncbi.nlm.nih.gov/articles/PMC11855051/
[4] K. Srinivas and V. Rao, “Real-Time Office Surveillance Using Computer Vision,” International Journal of Engineering Research and Technology, 2022. Source: https://www.researchgate.net/publication/382953872_A_Comprehensive_Survey_on_Smart_Wearables_Real-time_Health_and_Workplace_Monitoring_Alerting_and_Analysis_in_the_Industrial_Context
[5] L. Fernandes and T. Joseph, “Employee Productivity Dashboard System,” Springer International Conference on Data Analytics, 2023. Source: https://www.researchgate.net/publication/390661243wearableiotdeviceswithaiforoccupationalhealthreal-time_worker_monitoring_and_safety_analytics
[6] M. Awada, B. Becerik-Gerber, G. Lucas, and R. C. Roll, “Forecasting the Performance of Office Employees: A Machine Learning Perspective Involving Biological, Behavioral, and Psychological Parameters,” Sensors, 2023. Source: https://www.sciencedirect.com/science/article/pii/S187705092502633X
[7] “Computer Vision-based Employee Monitoring System: Face and Action Identification,” Procedia Computer Science, 2025. Source: https://www.sciencedirect.com/science/article/pii/S187705092502633X
[8] Y. H. Ko and I. G. Baek, “The Effect of Computer Monitoring on Employees’ Productivity in Telecommuting Arrangements,” Management Science, 2025. Source: https://pubsonline.informs.org/doi/10.1287/mnsc.2022.00588
[9] T. Mettler, “The Connected Workplace: Characteristics and Social Consequences of Work Surveillance in the Age of Datification, Sensorization and Artificial Intelligence,” Journal of Information Technology, 2024.: https://journals.sagepub.com/doi/10.1177/02683962231202535
[10] D. M. Ravid et al., “A Meta-Analysis of the Effects of Electronic Performance Monitoring on Work Outcomes,” Personnel Psychology, 2023. Source: https://onlinelibrary.wiley.com/doi/abs/10.1111/peps.12514
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This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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