Lower limb motor function assessment based on TensorFlow convolutional neural network and kernel entropy component analysis–local tangent space alignment

Abstract

Motor function assessment of patients and the elderly is crucial to gait assessment and gait rehabilitation. Accuracy of the assessment is affected by clinician’s experience. To solve the problem, this article proposes motor function assessment index to assess the motor function of patients. VICON system collects video of subjects when they are walking. And the original gait videos are pre-processed by the pixel-based adaptive segmenter and extracted by the convolutional neural network. The kernel entropy component analysis and local tangent space alignment reduced the dimensions of extracted features, and motor function assessment index is obtained. The Pearson correlation analysis shows that the motor function assessment index and modified gait abnormality rating scale are significantly correlated, and Pearson correlation coefficient is 0.92. These effectiveness results demonstrate that the proposed method has the considerable potential to promote the future design of automatic motor function assessment for clinical rehabilitation research.