PROACT: Anticipatory Action Modeling and Anomaly Prevention in Industrial Workflows

Human-Robot Collaboration (HRC) requires seamless interaction between humans and robots, necessitating an accurate understanding and response to human intentions, despite their unpredictability. This challenge is particularly acute in industrial settings, where the complexity of tasks and the variability of human actions demand robust solutions. Effective Human Activity Recognition (HAR) is essential, particularly where real-time anomaly detection is critical for maintaining productivity and efficiency. To address these challenges, we introduce PROACT, a novel framework that predicts operator actions and detects anomalies by constructing a reference graph from action sequences. By anticipating behavior and identifying deviations early, PROACT enhances safety and operational efficiency. The framework has been validated using the Meccano dataset, which simulates an industrial-like environment, demonstrating its capability in monitoring, proactive guidance, and anomaly detection.