Sitting POSTURE

Research on sitting posture focuses on accurately identifying and classifying various postures using computer vision and machine learning techniques, aiming to improve ergonomic assessments and prevent musculoskeletal disorders (MSDs). Current efforts employ diverse models, including convolutional neural networks (like ResNet), recurrent neural networks (like BiGRU), and spiking neural networks, often integrated with algorithms such as Mask R-CNN and YOLOv4 for image processing and classification. This work has significant implications for workplace safety, healthcare, and assistive technologies by enabling real-time posture monitoring, personalized feedback, and early risk assessment for MSDs. The development of robust and accurate posture recognition systems is crucial for improving human health and well-being.