Gait Activity Classification With Convolutional Neural Network Using Lower Limb Angle Measurement From Inertial Sensors

Human gait activity recognition can be crucial to adapt the assistance provided by lower limb exoskeletons, as well as for biomechanical analysis. With this purpose, deep learning techniques can be applied to develop a classifier based on the acquisition of the lower limb kinematics. In this article, we present a 1-D convolutional neural network (CNN) to classify different activities from the hip, knee, and ankle flexion/extension angles, measured with wearable inertial sensors. The proposed CNN classifier achieves 99.56% accuracy with users not involved in the learning process. In addition, the gradient-weighted class activation map (Grad-CAM) and the t-distributed stochastic neighbor embedding (t-SNE) were used to understand the CNN model decision-making. Finally, how the accuracy of the CNN model is impacted by input reduction was analyzed to adapt the CNN model to multiple situations, and it can be concluded that the CNN maintains high accuracy with a single joint angle as input.