Posture Recognition

Posture recognition, the automated identification of human body positions, aims to improve ergonomics, health monitoring, and human-robot interaction. Current research heavily utilizes machine learning, employing algorithms like Support Vector Machines, neural networks (including deep learning architectures like MLPs and LSTMs), and convolutional neural networks to analyze data from various sources, including pressure sensors, cameras (both 2D and 3D), and ultra-wideband (UWB) systems. These advancements enable real-time posture monitoring for applications such as risk assessment in manual labor, personalized feedback for improved sitting posture, and even emotion recognition. The resulting insights have significant implications for preventing musculoskeletal disorders, enhancing workplace safety, and creating more intuitive human-computer interfaces.