Near-duplicate human motion classification presents significant challenges due to the subtle differences and high similarity between actions. This paper introduces a posture estimation-based Gaussian Mixture Model (GMM) clustering algorithm as an enhancement to traditional pixel-based Convolutional Neural Networks (CNNs). The CNN architectures evaluated include ResNet-18, SqueezeNet, DenseNet, and MobileNet, which are used to classify images based on pixel data of images extracted from videos of participants performing chopping, sawing, and slicing tasks. While these CNN models perform well in extracting deep hierarchical features from images, differentiating between near-duplicate tasks can be challenging due to a lack of context in cross-frame human motion. In contrast, the posture-based approach focuses on capturing the spatial and temporal patterns of human body landmarks during task execution, using posture landmark points to classify human motion. Notably, posture-based task classification outperformed pixel-based task classification by 7.2%, with a lesser demand for image frame rate to achieve better accuracy. As the frames per second (FPS) increased from 1 to 30, the accuracy of posture-based classification improved from 76.3% at 1 FPS to 96.97% at 30 FPS. Additionally, we evaluated our model using the UCF Sports Action dataset as a benchmark to compare with state-of-the-art human task classification methods. These comparative analyses highlight the strengths and limitations of each approach, demonstrating that integrating posture data can enhance the classification accuracy of near-duplicate human motion.

Ergonomic suitability is critical in tasks involving musculoskeletal movement. Many industries have examined best practices and assessed workers' ergonomic conditions during physical tasks. Prolonged awkward postures are a known cause of discomfort and restricted mobility in areas such as the arms, spine, and neck. Technologies like computer vision and human activity recognition can help identify and prioritize ergonomic improvements. This paper presents findings from a two-decade scoping review on the role of automation and study design in ergonomic assessments of physical workplace tasks. Articles were sourced from Scopus, PubMed, IEEE Xplore, Engineering Village, and Google Scholar. Eighty-four studies were analyzed to evaluate the use of technologies in data collection, experimentation, analysis, and validation. We examined how participant variables (e.g. sample size, body part of interest) and validation accuracy impact study outcomes across domains. Integrating advanced technologies into ergonomic evaluations can enhance worker safety and productivity by supporting real-time, evidence-based decision-making.

Traditional approaches for human monitoring and motion recognition often rely on wearable sensors, which, while effective, are obtrusive and cause significant discomfort to workers. More recent approaches have employed unobtrusive, real-time sensing using cameras mounted in the manufacturing environment. While these methods generate large volumes of rich data, they require extensive labeling and analysis for machine learning applications. Additionally, these cameras frequently capture irrelevant environmental information, which can hinder the performance of deep learning algorithms. To address these limitations, this paper introduces a novel framework that leverages a contrastive learning approach to learn rich representations from raw images without the need for manual labeling. This framework mitigates the effect of environmental complexity by focusing on critical joint coordinates relevant to manufacturing tasks. This approach ensures that the model learns directly from human-specific data, effectively reducing the impact of the surrounding environment. A custom dataset of human subjects simulating various tasks in a workplace setting is used for training and evaluation. By fine-tuning the learned model for a downstream motion classification task, we achieve up to 90% accuracy, demonstrating the effectiveness of our proposed solution in real-time human motion monitoring.

OCCUPATIONAL APPLICATIONSResults from our exploratory study of restaurant worker mental models of injury and safety emphasize the need for improved occupational safety in the culinary industry through targeted interventions for chefs and managers. The analysis we performed showed that managers possess more integrated and coherent mental models of injury and safety than chefs, reflected in network parameters showing better organization of safety concepts. Kitchen training programs should focus on bridging gaps in safety awareness and mitigating hazards such as burns, cuts, slips, and equipment-related risks. Given their stronger understanding of safety protocols, managers are well positioned to lead kitchen staff training. Leadership-focused training for managers is vital for improving communication, enforcing protocols, and leveraging their integrated mental models of injury and safety in daily practice. Continuous education tailored to the specific responsibilities of chefs and managers is essential for ensuring effective safety training and maintaining standards.

OCCUPATIONAL APPLICATIONSThis study introduces an automated ergonomic assessment system tailored for repetitive culinary tasks. By combining joint tracking with the Assessment of Repetitive Tasks (ART) tool, the system identifies movement patterns and evaluates ergonomic risks. Results show strong alignment with expert evaluations. Operating with standard video input, the system minimizes the need for manual observation, enabling consistent and scalable risk evaluations. Designed for practical use, the system can be deployed in commercial kitchens using affordable cameras and minimal setup. The output aligns with ART categories, helping safety managers pinpoint high-risk tasks. These findings can inform ergonomic interventions, such as tool redesign or task rotation. User-friendly and requiring minimal training, the system is well-suited for small to medium-sized food operations. It facilitates early detection of musculoskeletal risks and complements existing safety protocols. With further development, the system holds potential for broader applications in fields like healthcare or manufacturing.