摘要
Background: Movement intentions are generally classified by Electroencephalogram (EEG) and have been used in gait initiation prediction. However, it is not easy to collect EEG data and practical in reality. Alternatively, ground reaction force (GRF) and the center of pressure (COP) is produced by the contact between the foot and the ground during a specific period of walking, which are the characteristics of evaluating gait performance Research question: The study aims to use a deep learning technique to recognize the data of the COP and GRF to classify straight walking and right turn. Second, the study aims to reveal gait characteristics that could replace EEG to predict walking directional intentions Methods: Ten healthy male adults were instructed to stand on the force platform and self-selected to perform three conditions: standstill, straight walking, and right turn. The onset of gait initiation was evaluated by muscle activation of the right tibialis anterior, and EEG and the COP displacement evaluated the onset of gait intention. Subsequently, GRF and COP would be treated as features to classify the gait intention in the Long Short-Term Memory (LSTM) model. Results: The results revealed that the onset of EEG and the COP displacement initiation were statistically significant differences between straight walking and right turn. For the classification, the average accuracy of the LSTM model with GRF and COP as features reached the highest one, 94.79 %, depending on the heel- or toe-off of the swing leg. The results indicated that gait intentions could be classified based on the GRF and COP. Significance: The machine learning technique of LSTM with gait parameters can recognize the gait intention of changing walking orientation. Our model and approach would be expected to provide advanced predictions, such as exoskeleton control or pedestrian traffic flow.