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In recent years, gait prediction has gradually become a cutting-edge research direction in the fields of biomechanics and artificial intelligence. Gait prediction technology, which analyzes an individual’s walking patterns to predict future changes, is crucial for the precision of rehabilitation and exoskeleton robot control. This paper reviews the recent research progress in the field of gait prediction, focusing on the multimodal information acquisition methods based on physical sensors and bioelectric signals, as well as the application of machine learning and deep learning algorithms in gait prediction. By analyzing different sensor data fusion strategies, the importance of multimodal information fusion for improving the accuracy of gait prediction is emphasized. Furthermore, this paper introduces the performance of traditional machine learning algorithms such as Support Vector Machine, Random Forest, and Back Propagation Neural Network, as well as deep learning models such as Long Short-Term Memory, Convolutional Neural Network, and Transformer in gait prediction, highlighting the advantages of deep learning in feature extraction and adaptability to complex scenarios. Finally, this paper explores future directions for the development of gait prediction technology, emphasizing improvements in timeliness, accuracy, and personalization to advance exoskeleton robotics and related fields.
Keywords: Gait prediction, multimodal information acquisition, machine learning, deep learning
