A Combined Low-Rank Matrix Completion and CDBN-LSTM Based Classification Model for Lower Limb Motion Function

Assessment of lower limb motion function is crucial for clinicians to treat patients effectively. However, diagnosis accuracy is still affected by clinical experience. To reduce the influence of subjective factors, a new classification model using conditional deep belief network (CDBN) and long short-term memory (LSTM) is proposed. Firstly, the original data of gait are collected by motion capture system, and low-rank matrix completion is utilized to reconstruct missing data caused by the interruption of hands and clothes. Then CDBN is trained for extracting features and LSTM classifies the features from the completed data. Compared with deep belief network (DBN), CDBN is based on conditional restricted Boltzmann machine (CRBM) which improves the importance of past time-step to achieve higher accuracy for temporal sequences. And LSTM classifies features with time information. The matrix completion result verifies excellent recovery accuracy of the proposed method. Experimental results demonstrate that CDBN-LSTM is 4% higher than that of DBN. In addition, the proposed model outperforms traditional neural network on the train process and precision rate.