# 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% \(\pm\) 0.955%, and the average recognition delay rate is only 25.017% \(\pm\) 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. exoskeleton robot; locomotion mode recognition; finite state machine; hierarchical support vector machine +
Footnote †: journal: Applied Sciences  
Article  
FSM-HSVM-Based Locomotion Mode Recognition for Exoskeleton Robot  
[PERSON]; [PERSON]; [PERSON]; [PERSON] FSM-HSVM-Based Locomotion Mode Recognition for Exoskeleton Robot. [PERSON] et al. [10] proposed that classification could be performed using the discriminant function or if-else rule set, the learning algorithm derived from the training example, and a priori knowledge to reduce the average classification error to 11%. According to the non-stationary characteristics of leg EMG signals during movement, a new phase-related EMG PR strategy was proposed to classify the user's movement patterns; the average classification errors in the four defined phases using ten electrodes placed over the muscles above the knee were 12.4%, 6.0%, 7.5%, and 5.2%, respectively [11]. However, the electrode for EMG signal acquisition had to be affixed to the skin surface of wearer, which was not only inconvenient in practice but also disrupted by perspiration [12]. This method is simple and straightforward to realize the benefits of low interference [13], as the mechanical sensor is primarily on the exoskeleton machinery. Specifically, the multi-source information fusion technology that integrates various types of sensors can obtain more information than a single type of sensor, thereby enhancing recognition performance [14,15]. [PERSON] et al. [16] proposed a motion patterns recognition method based on convolutional neural network (CNN) and strain gauge signals, identifying three motion patterns of flat, ascending terrain and descending terrains with a 92.06% overall recognition accuracy. In the literature, [PERSON] et al. developed a pure mechanical sensor architecture consisting of accelerometers, gyroscopes and pressure insole sensors [17]. The Cartesian product of five terrain types and three walking speeds defined 15 sample sets of motion intention. The measured real-time data were used to calculate the inter-observer reliability (ICC) with the template data and the Dempster-Shafer (D-S) data fusion theory, and the hidden Markov model (HMM) was used to recognize the real-time motion state with an average accuracy of 95.8% based on the reference to the previous step. However, there was neither continuous processing of motion data, nor mutual recognition of terrain pattern conversions. [PERSON] et al.