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Secondly, the acquired gait data is analyzed with a novel hybrid prediction model to assess the gait anomalies quantitatively and objectively. In the hybrid model, the extracted features of gait data contain the manually-extracted features and the automatically-extracted features from Long Short-Term Memory network. Experimental results on real patients demonstrate that the proposed gait analysis system can quantitatively predict gait anomalies with a high accuracy of 98.77 %. Therefore, this gait acquisition and analysis system achieve