Worker Behavior

Research on worker behavior focuses on understanding and improving human performance and safety in various work settings. Current studies employ computer vision, machine learning (including Gaussian process hidden semi-Markov models and K-modes clustering), and multimodal sensor data analysis to quantify motion, extract behavioral patterns, and even predict personality traits from speech and physiological signals. These advancements enable more efficient worker training, improved ergonomic design, and enhanced safety protocols by identifying potential hazards through simulation and real-time monitoring of worker actions. The ultimate goal is to increase productivity and well-being in the workplace through data-driven insights.