# FSM-HSVM-Based Locomotion Mode Recognition for Exoskeleton Robot
## 4 Discussion  
The experimental results show that the average recognition accuracy of FSM-HSVM for five different motion modes is 97.106% \(\pm\) 0.955%, whereas the average recognition accuracies of SVM and HSVM algorithms are 90.826% \(\pm\) 2.391% and 92.807% \(\pm\) 1.927% respectively. In comparison with the other two algorithms, the average recognition rate increased by 6.28% and 5.036%, while the standard error decreased by 1.43% and 0.972%. Evidently, the algorithm proposed in this paper demonstrates superior recognition efficiency and recognition stability compared to SVM and HSVM. However, the results also show that the recognition accuracy of the three kinds of flat walking is the lowest, and there is a high likelihood that they are misidentified as walking down the ramp. The shortest distance, according to the DTW distance table, is between the FW and DR. It demonstrates conclusively that the similarity between the two modes is high and difficult to distinguish. This is likely due to the slope selected in this paper being only about 10\({}^{\circ}\), making it virtually identical to the flat ground. At present, some good progress has been made in recognition accuracy in motion pattern recognition [18; 19]. The recognition accuracy of reference [18] is as high as 98.35% \(\pm\) 1.65%. However, on the premise of higher slope and easier detection, there are still obvious misjudments in the process of stable motion pattern recognition, and the stability is weaker than that of the algorithm proposed in this paper. In reference [19], the finite state machine was used to limit the state transition for post-processing to improve the overall performance, but it was not enough for only three kinds of terrain, the common slope terrain in daily life should especially be considered. In conclusion, the proposed algorithm has better stability compared to references [18; 19]. In practice, the RRDs of eight transitions between locomotion mode are extremely important. According to Table 5, we can find that the RRD is the smallest when the flat walking is converted to descending stairs,  
Figure 7: Comparison of recognition time of three algorithms. with a value of 5.333% \(\pm\) 1.687%. This demonstrates that the characteristics of the transition period between flat ground and down stairs change significantly, and the algorithm is highly sensitive. The highest delay rate was 53.733% \(\pm\) 9.363%, which occurred during the period from down the ramp to flat walking, and the second highest was 43.067% \(\pm\) 4.107% when going from up the ramp to flat walking. This demonstrates that the feature change from ramp to ground is ambiguous, and the algorithm recognizes it only in the middle of the transition. According to that mentioned above, it is obvious that the slope gradient is small and is not different from the flat walking. The lack of prominent data collection characteristics during the transition period may be the primary cause of the high delay rate. The overall recognition delay rate is 25.017% \(\pm\) 6.074%, which is significantly less than one step, indicating that the corresponding large motion mode can be recognized in the middle of swing. The conversion between these motion modes can also be accomplished through prior knowledge of the wearer or pre control, allowing the robot exoskeleton system to respond in advance, thereby ensuring system stability. In terms of the algorithm's recognition efficiency, the experimental results indicate that the motion time is reduced by approximately 8s and 3s compared to SVM and HSVM, respectively. Through theoretical calculation and analysis, it is also determined that the FSM-HSVM algorithm is significantly less effective than the HSVM algorithm when using SVM to solve multiclassification problems. In addition, Table 2 demonstrates that HSVM's recognition efficiency is superior to that of other multi-classification SVM algorithms. Among the multi-classification SVM algorithms discussed in this paper, FSM-HSVM has the highest recognition efficiency.