FSM-HSVM-Based Locomotion Mode Recognition for Exoskeleton Robot

Abstract

This paper proposes a hierarchical support vector machine recognition algorithm based on a finite state machine (FSM-HSVM) to accurately and reliably recognize the locomotion mode recognition of an exoskeleton robot. As input signals, this method utilizes the angle information of the hip joint and knee joint collected by inertial sensing units (IMUs) on the thighs and shanks of the exoskeleton and the plantar pressure information collected by force sensitive resistors (FSRs) are used as input signals. This method establishes a framework for mode transition by combining the finite state machine (FSM) with the common locomotion modes. The hierarchical support vector machine (HSVM) recognition model is then tightly integrated with the mode transition framework to recognize five typical locomotion modes and eight locomotion mode transitions in real-time. The algorithm not only reduces the abrupt change in the recognition of locomotion mode, but also significantly improves the recognition efficiency. To evaluate recognition performance, separate experiments are conducted on six subjects. According to the results, the average accuracy of all motion modes is 97.106% ± 0.955%, and the average recognition delay rate is only 25.017% ± 6.074%. This method has the benefits of a small calculation amount and high recognition efficiency, and it can be applied extensively in the field of robotics.