A Novel FSVM with PSO for gait phase detection based on elastic pressure sensing insole system

Abstract

The precise gait phase detection with lightweight equipment under variable conditions is crucial for low limb exoskeleton robots. Therefore, the kinematics and dynamics information are investigated. In this paper, a novel radius-margin-based support vector machine (SVM) model with particle swarm optimization (PSO) in feature space called PSO-FSVM is proposed for gait phase detection. The proposed method addresses the dual objectives of maximizing margin while minimizing radius, employing PSO to fine-tune the parameters of the FSVM. This enhancement significantly bolsters the classification accuracy of the SVM. For the measurement of gait features with a lightweight sensor system, the plantar pressure insoles equipped with flexible and elastic sensors are designed. To evaluate the effectiveness of our method, we conducted comparative experiments, pitting the proposed PSO-FSVM against other support vector machine variants, across four treadmill speeds. The experimental results indicate that the proposed method achieves an accuracy of over 98% at four different speeds indoors. Furthermore, the proposed method is compared with other algorithms (SVM, k-nearest neighbor (KNN), adaptive boosting (AdaBoost), and quadratic discriminant analysis (QDA)) under outdoor experiments. The experimental results demonstrate that the average recognition accuracy of this method reaches 96.13% under variable speed conditions, with an average accuracy of 98.06% under slow walking conditions, surpassing the performance of the above four algorithms.