Gait Pattern

Gait pattern analysis focuses on understanding and modeling the movement patterns of humans and robots during locomotion, aiming to improve efficiency, adaptability, and diagnostic capabilities. Current research emphasizes the use of machine learning, particularly deep learning models like convolutional neural networks and transformers, along with techniques like Bayesian optimization and Monte Carlo Tree Search, to analyze gait data from various sensors (inertial, force plates, video). This research is significant for applications ranging from personalized medicine (detecting gait disorders and predicting medication effectiveness) to robotics (designing more robust and adaptable locomotion systems) and human activity recognition.