# FSM-HSVM-Based Locomotion Mode Recognition for Exoskeleton Robot
## 2 Materials and Methods
### Data Source  
The following is a unified definition of the concepts related to locomotion mode to facilitate discussion. The human body's locomotion modes fall into two categories: stable locomotion mode and locomotion mode transition. Stable locomotion mode refers to continuous motion in a single locomotion mode, whereas locomotion mode transition refers to the rapid transition from one locomotion mode to another during motion. The transition time of the locomotion mode is defined uniformly as the moment when the leading foot of the previous mode leaves the ground, and the cycle from this time until the next time this foot first leaves the ground in the next mode is known as the locomotion mode transition period.  
A total of five subjects volunteered to participate in this experiment, and the height of these subjects was 1.60-1.80 m and the weight was 60-80 kg. Before the experiment, all participants were informed of all experimental procedures and had no diseases. Before the experiment, the connection between each structural member and the exoskeleton's information acquisition system was checked to ensure that the sensor is calibrated and in a standard state. When a subject is wearing an exoskeleton, the length of the exoskeleton rod is adjusted based on the subject's feelings. Then the subjects walked adaptively for approximately one minute, after which they continued to fine-tune their gait to maximize human comfort and flexibility. Each subject wore an exoskeleton robot and conducted two types of experiments at a speed that was approximately constant and without load. The initial experiment consists of a stable mode experiment with various locomotion modes. As shown in Figure 2, the subjects walk in five modes: flat walking (FW), up the stairs (US), down the stairs (DS), up the ramp (UR) and down the ramp (DR). The staircase is 1.5 m wide, 40 cm deep, 15 cm tall, and slopes at about 26\({}^{\circ}\). The ramp slope is approximately 10\({}^{\circ}\), and its length is 4.5 m. Each exercise mode included five experiments for one subject, each lasting about thirty seconds. The second experiment involves the transition between various locomotion modes. Each experimenter walks from the flat ground to another locomotion mode, and from other modes to the flat ground, with a total of eight conversion modes, i.e., FW\(\rightarrow\)US, US\(\rightarrow\)FW, FW\(\rightarrow\)DS, DS\(\rightarrow\)FW, FW\(\rightarrow\)UP, UP\(\rightarrow\)FW, FW\(\rightarrow\)DP, DP\(\rightarrow\)FW. Each transition period included five experiments on one subject lasting thirty seconds.  
Figure 1: The lower extremity exoskeleton robot of BIT.  
Once sufficient training datasets had been obtained, the FSM-HSVM model could be trained offline. To remove noise and interference from the data collected by the acquisition system, a second-order Butterworth low-pass filter with a cutoff frequency of 5 Hz was applied. Locomotion mode recognition was performed in real-time and online. Text was used to record the recognized pattern in the processor, which was then verified and analyzed after the experiment was completed. All experiments were approved by the Beijing institute of technology's Medical and Experimental Animal Ethics Committee, and the procedures used in this study followed the specified principles.