# FSM-HSVM-Based Locomotion Mode Recognition for Exoskeleton Robot
## 3 Results  
### Experimental Results and Analysis of Locomotion Mode Recognition  
In order to confirm the efficacy of the FSM-HSVM locomotion mode recognition model, the confusion matrix is used to evaluate the performance of the model using the same motion dataset. Five human subjects were required to walk at a similar speed over varied terrain. The identification motion mode label is recorded in the control software using three distinct techniques: SVM, HSVM and FSM-HSVM. As mentioned previously, the confusion matrix can be used to display the recognition accuracy. In order to study the stability and statistical law of these algorithms, it is necessary to further deal with the confusion matrix of all subjects, and the average value MEAN of each element and its corresponding standard error SEM are calculated respectively. So, the elements of the confusion matrix are expressed as MEAN \(\pm\) SEM in Tables 3-5 below. As shown in Table 3 above, the highest recognition accuracy occurs in down ramp mode with a 95.948% recognition accuracy. In flat walking, the lowest recognition accuracy is only about 83.658%. Table 4 shows the recognition accuracy for HSVM, with the highest recognition accuracy being 96.201% when going up the stairs. The recognition accuracy of flat walking remains the lowest. According to Table 5, the accuracy of motion pattern recognition exceeds 93.420%. In particular, up the stair mode has a recognition rate of 98.760%. In comparison to Tables 3 and 4, the results demonstrate that FSM-HSVM has higher recognition accuracy. Figure 6 depicts the recognition accuracy of three algorithms. The FSM-HSVM algorithm has achieved the highest recognition accuracy across five different locomotion modes, with an average accuracy of 97.106% \(\pm\) 0.955%. In addition, the flat walking mode has the lowest recognition accuracy of the three algorithms. Review the Tables 3-5 reveals that walking on flat ground is easily confused with walking down the ramp. Even with the FSM-HSVM algorithm, the probability is as high as 3.907%. The performance index of RRD is used to evaluate the real-time performance of the proposed recognition strategy. In this paper, Table 6 shows the RDR for the eight conversion modes. If RRD is positive, it indicates that the correct identification time is later than the actual transition time. If RDR is negative, the correct recognition time is in advance of the actual transition time. According to Table 5, the RDRs for all eight transitions between locomotion modes are positive, indicating that the correct recognition under all locomotion modes lags behind the actual conversion time. Especially for DR\(\rightarrow\)FW conversion mode, the RRD can reach 53.733%. The following is UR\(\rightarrow\)FW locomotion mode transition, and the delay rate is 43.067%. In other words, it can be distinguished by the middle of the whole cycle when the ramp is transitioned to flat walking. The lowest RRD is only 5.333% when FW\(\rightarrow\)DS, and it indicated that the leg is correctly recognized in the early stage of swing. The average RRD is 25.017% \(\pm\) 6.074% under all locomotion mode transitions. According to research, the swing period accounts for about 40% of the whole gait cycle, and the precise transition occurs roughly in the middle of the swing period.