Walking Gait Classification Algorithm for Exoskeleton Robot on Unstructured Ground

For ground reaction force (GRF), there exists huge difference on different terrains because of human leg's random movement and unstructured ground. Therefore, an experiment shoe with insole-type pressure sensors is developed for detecting GRF in real time, and a PSO-SVM (particle swarm optimization based support vector machine) algorithm for gait classification is presented. 7 pressure sensors are installed into the shoes redundantly according to the plantar pressure distribution. The GRFs when a person walks on treadmill (5 km/h), concrete pavement and field grass are collected and processed. Corresponding labels are set in advance for the GRFs from the basic group, which is used as training set. Based on the training set, an ordinary classifier I and a classifier Ⅱ based on PSO-SVM algorithm are constructed to classifying the walking data on concrete pavement and field grass respectively. The experiment results demonstrate that, compared with the ordinary classifier I, the proposed optimization algorithm shows a great advantage of gait classification on complex terrains, gait identification accuracies on concrete pavement and field grass are improved by 32.9% and 42.8% respectively, and still a fast optimization speed and a good robustness are implemented after some insole-type pressure sensors malfunction.