Industrial human-robot-collaboration (HRC) depends on under-standing how people act, stay engaged, and work together with robots during shared tasks. In most prior work, these aspects are studied in isolation, which makes it hard to see the full picture of real collaboration. This study tackles that problem with an improved multimodal framework that jointly captures fine-grained human actions, engagement levels, and collaboration outcomes in an industrial assembly setting. It introduces a three-layer analytical model called MICRO-MESO-MACRO (M3), which links detailed action patterns to engagement dynamics and overall system efficiency. All data streams are aligned in time using a custom synchronization process so that visual, motion, and behavioral signals can be analyzed together in a consistent way. The results show clear relationships between varied action patterns, stable engagement, and better collaboration efficiency, providing a solid foundation for designing adaptive, human-aware robotic partners. The annotated dataset and code are available at: https://github.com/arvindsihag/m3_analyzer.

Detecting and interpreting operator actions, engagement, and object interactions in dynamic industrial workflows remains a significant challenge in human-robot collaboration research, especially within complex, real-world environments. Traditional unimodal methods often fall short of capturing the intricacies of these unstructured industrial settings. To address this gap, we present a novel Multimodal Industrial Activity Monitoring (MIAM) dataset that captures realistic assembly and disassembly tasks, facilitating the evaluation of key meta-tasks such as action localization, object interaction, and engagement prediction. The dataset comprises multi-view RGB, depth, and Inertial Measurement Unit (IMU) data collected from 22 sessions, amounting to 290 minutes of untrimmed video, annotated in detail for task performance and operator behavior. Its distinctiveness lies in the integration of multiple data modalities and its emphasis on real-world, untrimmed industrial workflows-key for advancing research in human-robot collaboration and operator monitoring. Additionally, we propose a multimodal network that fuses RGB frames, IMU data, and skeleton sequences to predict engagement levels during industrial tasks. Our approach improves the accuracy of recognizing engagement states, providing a robust solution for monitoring operator performance in dynamic industrial environments. The dataset and code can be accessed from https://github.com/navalkishoremehta95/MIAM/.

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.