https://doi.org/10.1051/epjconf/202532801053
AI-Powered Human Activity Recognition for Real-Time Detection of Inactive Workers in the Workplace
1 Ramdeobaba University, Computer Science and Engineering Department, Nagpur, Maharashtra, India
2 Ramdeobaba University, Computer Science and Engineering Department, Nagpur, Maharashtra, India
* Corresponding author: sayeedaa@rknec.edu
Published online: 18 June 2025
This research presents a novel way to identify idle employees (non or low productivity) that has not been used in the prior literature utilizing Human Activity Recognition (HAR) in a YOLOv11-CNN (Convolution Neural Networks) detection framework. The model was trained and validated using three datasets from RoboFlow Universe which focused on workplace scenarios and was specifically designed to identify "active" and "sleeping" modes of employee non or low productivity. The main findings show that the model demonstrated a mAP@50 of 97.8% (Dataset 1) with average accuracy of 89.3%and peaked at a F1-score of 0.89, associated with a confidence threshold of 0.569 (Dataset 3). Importantly, the precision and recall curves displayed and no confusion matrices indicated consistent model estimating across confidence thresholds and class-wise performance. Overall, the training displayed smooth convergence and slight overfitting demonstrating potential to transfer into the real-world workplace to monitor employee productivity.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
