Gait Phase

Gait phase detection involves identifying the distinct stages of a walking cycle, crucial for controlling assistive devices like prosthetics and exoskeletons. Current research focuses on developing accurate and efficient algorithms, employing machine learning techniques (including deep learning architectures such as convolutional neural networks and recurrent neural networks) and leveraging data from various sensors (e.g., IMUs, EMG). Accurate gait phase recognition is vital for improving the performance, safety, and adaptability of these devices, impacting both biomechanical research and the development of advanced rehabilitation technologies.