# SA-SVM-Based Locomotion Pattern Recognition for Exoskeleton Robot
## 4 Discussion  
A SVM algorithm based on SA was proposed to recognize the different locomotion patterns of the exoskeleton robot. The finite state machine was used to improve the identification accuracy in the proposed algorithm. Experimental results on six healthy human subjects demonstrated the effectiveness of the proposed method for locomotion pattern recognition.  
The proposed method needs two pressure sensors in the shoes and four inertial measurement units on the mechanical parts and there is no need to mount any sensors on the human body, which is very convenient in actual applications. In the proposed method, the three locomotion patterns of level-ground walking, stairs ascent and stairs descent were identified. The accuracy of locomotion patterns recognition used SA-SVM is 94.8 \(\pm\) 0.75%, and the accuracy of locomotion patterns recognition used SA-SVM combined with FSM is 97.47% \(\pm\) 1.16%. These sensors are placed on mechanical connecting rods to reduce fatigue for the subjects, which are suitable for long periods use. The results show that the proposed method can correctly identify the three locomotion patterns, and the average recognition delay is 26.43% \(\pm\) 4.62% of one gait cycle, which is much less than one step. Generally speaking, the recognition results of locomotion patterns can be obtained in advance to allow sufficient reaction time for the subsequent control of the exoskeleton robot.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **LG** & **SAT** & **SDt** \\ \hline
**LG** & 95.9\% \(\pm\) 0.71\% & 1.63\% \(\pm\) 0.2\% & 5.1\% \(\pm\) 0.62\% \\
**SAT** & 1.85\% \(\pm\) 0.07\% & 96.4\% \(\pm\) 1.14\% & 1.35\% \(\pm\) 0.09\% \\
**SDt** & 5.44\% \(\pm\) 0.56\% & 1.17\% \(\pm\) 0.29\% & 92.1\% \(\pm\) 0.39\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Confusion matrix of SA-SVM experimental results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **LG** & **SAT** & **SDt** \\ \hline
**LG** & 98.9\% \(\pm\) 0.85\% & 1.02\% \(\pm\) 0.37\% & 3.6\% \(\pm\) 0.44\% \\
**SAT** & 1.24\% \(\pm\) 0.05\% & 97.4\% \(\pm\) 1.75\% & 0.56\% \(\pm\) 0.12\% \\
**SDt** & 3.96\% \(\pm\) 0.2\% & 0.47\% \(\pm\) 0.18\% & 96.1\% \(\pm\) 0.88\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Confusion matrix of SA-SVM with FSM experimental results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **LG** & **SAT** & **SDt** \\ \hline
**LG** & 91.04\% \(\pm\) 2.7\% & 2.14\% \(\pm\) 0.32\% & 8.5\% \(\pm\) 1.06\% \\
**SAT** & 1.7\% \(\pm\) 0.55\% & 89.64\% \(\pm\) 2.3\% & 0.9\% \(\pm\) 0.41\% \\
**SDt** & 7.96\% \(\pm\) 1.3\% & 1.3\% \(\pm\) 0.19\% & 87.04\% \(\pm\) 1.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Confusion matrix of SVM experimental results without SA.  
The proposed method needs to be improved to solve some problems. In the locomotion patterns transition state, the recognition accuracy is not high, which may lead to error of the exoskeleton control. The recognition delay of stairs descent and stairs descent transition state is about 30%, which may lead to the control lag of the exoskeleton robot. In practical application, we adopt the method of pre-control in the control part to solve the above problems of the locomotion pattern recognition, which ensures the stable control of the exoskeleton robot.