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.

Hierarchical Classification of Subject-Cooperative Control Strategies for Lower Limb Exoskeletons in Gait Rehabilitation: A Systematic Review

“…These sensors are crucial in determining various gait features, such as phase, step length, cadence, etc. In particular, certain supervisory control schemes, such as Finite State Machine (FSM) with FSR [30,46,52] and IMU [28,53,64,66], utilize gait phases as additional inputs to the high-level control in combination with reference joint trajectories. For example, Chen et al [46] implemented an FSM-based supervisory control in which the FSR and encoders (velocity) were employed to discretize the gait cycle into different phases.…”

“…As depicted in Figure 3, the gait cycle can be divided into four different patterns based on FSR and velocity readings: (1) two phases-stance or swing; (2) three phases-stance, early swing, or late swing; (3) four phases-early stance, late stance, early swing, or late swing; and (4) five phases-early stance, mid-stance, late stance, early swing, or late swing. In another work by Qi et al [66], a hierarchical support vector machine recognition algorithm was proposed for accurate and reliable locomotion mode recognition in an exoskeleton robot. The proposed algorithm combined the FSM with input signals from IMUs and FSRs to establish a mode transition framework.…”