Human Motion Analysis

Human motion analysis focuses on understanding and interpreting human movement through various data sources, aiming to improve applications ranging from healthcare to robotics. Current research emphasizes developing robust and interpretable models, often employing graph convolutional networks, Gaussian mixture models, and deep learning architectures like LSTMs, to analyze motion capture data (including RGB, RGB-D, thermal, and inertial sensor data) and extract meaningful features for tasks such as action recognition, gait analysis, and rehabilitation assessment. This field is significant due to its potential for creating more effective diagnostic tools, personalized rehabilitation programs, and human-robot interaction systems, driving advancements in healthcare, assistive technologies, and human-computer interaction.