Real-Time Employee Monitoring and Fatigue Analysis

To achieve employee productivity in the remote and hybrid work environments, it is getting harder for the organisations to handle issues concerning fatigue and inattention. The paper also presents a Real Time Employee Monitoring and Fatigue Analysis System that monitors employee performance, detects signs of fatigue, and analyzes their emotional states. Live video capture is done through the system using OpenCV’ and object detection is done using Haar cascade classifiers; eye states and facial emotions are being classified using Convolutional Neural Networks (CNNs). The dataset used for training the model consists of 60000 images, We achieve 97% accuracy in eye state detection and 61% accuracy in emotion prediction. Besides monitoring fatigue and emotions, the system reports failure to detect internet connectivity in the event of a possible network disruption. CSV format reports are generated to assist Human Resource (HR) Team in analyzing employee well-being. The system offers real time insights to provide organizations with the insights to take timely interventions, which eventually promote a healthy work environment and maintains high levels of productivity.