Evaluation of CNN algorithm at locomotion mode identification for a knee-assisted exoskeleton

Abstract

In this study, a knee-assisted exoskeleton that can assist the wearer's knee joints at the appropriate phase of the gait cycle is designed. A convolutional neural network (CNN) model is used in the gait control to identify the locomotion modes of the exoskeleton. In order to verify the CNN-based locomotion mode identification method, an offline CNN model was created in MATLAB. The experiment was conducted and seven healthy subjects’ hip, knee, and ankle joint angles were captured and six common locomotion modes were recognized in the experiments. The recorded data were segmented with overlapping sliding windows of varying sizes and were tested using a stratified 5-fold cross-validation technique. The experimental findings showed that the appropriate window size was 1.25T (T stands for the normal adult gait cycle). The lowest and the highest accuracies of the test sets at the window size of 1.25T were 96.47% ± 0.83% and 99.67% ± 0.49% on different subjects. It also showed that the model has strong generalization. Thus, the algorithm had proved useful to extract the characteristics of lower limb joint angles in various locomotion modes and the approach in this paper can be extended to the recognition of motion patterns in lower limb exoskeletons.