Gait Prediction
Gait prediction research focuses on accurately forecasting human movement patterns, primarily to aid in diagnosing neurological disorders and improving the control of exoskeletons. Current efforts utilize various deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, often incorporating attention mechanisms to capture temporal dependencies in gait data from wearable sensors or motion capture systems. These advancements hold significant promise for improving the diagnosis and monitoring of conditions like Parkinson's disease, facilitating more effective rehabilitation strategies, and enhancing the performance of robotic exoskeletons.
Papers
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