The study of a novel deep learning-based framework for risk identification and real-time ergonomic posture assessment in computer-driven environments

The necessity for sophisticated and scalable ergonomic evaluation methods is highlighted by the rise in posture-related musculoskeletal problems brought on by a growing emphasis on computing-driven work. Conventional methods of evaluation, which rely on checklist-derived analysis and manual observations, are unable to provide ongoing, impartial assessment. Automation methods for postural detection and ergonomic risk assessment have drawn a lot of consideration due to advancements in artificial intelligence, especially in deep learning and computer vision. Recent techniques, such as sensor-based methods, vision-based strategies, and advanced algorithms for deep learning, including convolutional, recurrent, and transformer models, are all thoroughly covered in this review. Additionally, it examines current advancements in adaptive learning and multimodal interface that improve real-time functionality and customization. According to the investigation, issues with interpretability, robustness, and deployment in real-world settings continue to exist even as deep learning outcomes increase accuracy and scalability. Additionally, the article outlines implied research directions, including as privacy-aware learning frameworks, multimodal data fusion, and resolution-based AI methods, and indicates investigation gaps. All things considered, this analysis provides invaluable insight into current developments and encourages the creation of intelligent ergonomic solutions targeted at enhancing workplace happiness and proactive medical care