# FSM-HSVM-Based Locomotion Mode Recognition for Exoskeleton Robot
###### Abstract
used particle swarm optimization (PSO) to optimize support vector machine (SVM) for classification and identification based on plantar pressure signal and leg posture signal, with a 96.00% \(\pm\) 2.45% accuracy rate. Following this, the majority voting algorithm (MVA) was utilized for post-processing, and the recognition accuracy reached 98.35% \(\pm\) 1.65% [18]. The paper [19] proposed a support vector machine model based on simulated annealing algorithm to identify three distinct motion modes. It then used a finite state machine to limit state transition for post-processing to improve the overall performance of the system, with recognition accuracy reaching 97.47% \(\pm\) 1.16%. [PERSON] et al. used a rule-based classification algorithm to achieve real-time recognition of the three most common motion modes of flat walking, stair climbing and stair descending. The average accuracy of recognition reached 98.2%, and the average delay in recognition for all conversions was slightly less than one step [20]. Regrettably, these studies did not conduct a more in-depth examination of the miscalculation in the stable locomotion mode and the computational efficiency. Various algorithms, such as Latent Dirichlet Allocation (LDA), Bayesian algorithm network, neural network, support vector machine (SVM), and so on, are commonly used to solve classification problems. The SVM model is a two-classification model; for multi-classification problems, additional improvements [21, 22, 23, 24] are required. Commonly used techniques include one to one (OVO) [21], one to rest (OVR) [22], directed acyclic graph SVMs (DAG SVMs) [23], hierarchical support vector machines (HSVM) [24], and others. Undoubtedly, these strategies will increase the amount of computation to varying degrees, resulting in a decrease in algorithm efficiency. Reference [25] proposed a design method of hierarchical support vector machine multi-class classifier based on the inter-class separability. Experimental results indicated that this method increased the classification accuracy and accelerated the classification speed. We propose a hierarchical support vector machine algorithm based on finite state machines (FSM-HSVM). The algorithm combines the finite state machine with common locomotion modes to create a mode framework, then combines this framework with the hierarchical support vector machine recognition model. Hip angle, knee angle and plantar pressure data collected by IMUs and FSRs are preprocessed as a training signal input. Then, for real-time recognition, one or all sub-classifiers in the HSVM model are invoked in the FSM framework. To validate the efficacy and precision of the proposed algorithm, a large number of experiments are conducted under five typical motion modes and eight locomotion mode transitions. The organizational structure of this paper is as follows. Section 2 first introduces the structure, data acquisition system and data source of exoskeleton robot, then introduces HSVM theory and FSM theory, as well as the process of recognition algorithm for motion pattern recognition, and finally introduces the evaluation index of algorithm performance. Sections 3 and 4, respectively, present the experimental results and discussion. The conclusion is presented in Section 5.