# Crouch Gait Recognition in the Anatomical Space Using Synthetic Gait Data
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**KNN**} & \multicolumn{4}{c}{**NB**} & \multicolumn{4}{c}{**DA**} \\ \hline
**Metric** & **N** & **C1** & **C2** & **C3** & **C4** & **N** & **C1** & **C2** & **C3** & **C4** & **N** & **C1** & **C2** & **C3** & **C4** \\ \hline Acc & 99.40 & 98.60 & 99.60 & 98.60 & 99.80 & 98.60 & 99.20 & 98.60 & 99.60 & 98.80 & 99.80 & 99.60 & 100.00 & 99.40 & 100.00 \\ R & 100.00 & 98.57 & 97.98 & 95.00 & 99.00 & 94.31 & 100.00 & 95.84 & 98.33 & 97.08 & 98.57 & 98.26 & 100.00 & 100.00 & 100.00 \\ SP & 99.26 & 98.61 & 100.00 & 99.50 & 100.00 & 99.51 & 99.01 & 99.23 & 99.76 & 99.20 & 100.00 & 100.00 & 100.00 & 99.26 & 100.00 \\ P & 96.92 & 97.06 & 100.00 & 97.14 & 100.00 & 97.14 & 96.36 & 97.27 & 98.89 & 97.60 & 100.00 & 100.00 & 100.00 & 96.90 & 100.00 \\ FM & 98.37 & 97.74 & 98.94 & 95.88 & 99.47 & 95.58 & 98.04 & 96.50 & 98.50 & 97.18 & 99.23 & 99.09 & 100.00 & 98.36 & 100.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Classification performance of 5 gaits using left lower limb with KNN, NB, and DA. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**KNN**} & \multicolumn{4}{c}{**NB**} & \multicolumn{4}{c}{**DA**} \\ \hline
**Metric** & **N** & **C1** & **C2** & **C3** & **C4** & **N** & **C1** & **C2** & **C3** & **C4** & **N** & **C1** & **C2** & **C3** & **C4** \\ \hline Acc & 99.20 & 99.20 & 99.80 & 98.60 & 99.20 & 99.60 & 99.80 & 99.60 & 99.20 & 99.40 & 99.40 & 96.2 & 95.4 & 98.6 & 98.00 \\ R & 97.55 & 97.00 & 99.23 & 99.00 & 97.67 & 97.98 & 100.00 & 100.00 & 94.97 & 100.00 & 99.09 & 91.94 & 87.62 & 96.09 & 94.56 \\ SP & 99.75 & 99.75 & 100.00 & 98.55 & 99.75 & 100.00 & 99.76 & 99.50 & 100.00 & 99.26 & 99.50 & 97.47 & 97.36 & 99.24 & 98.72 \\ P & 99.09 & 98.75 & 100.00 & 94.42 & 99.09 & 100.00 & 99.00 & 98.04 & 100.00 & 96.92 & 98.09 & 90.56 & 90.00 & 96.87 & 94.95 \\ FM & 98.22 & 97.78 & 99.60 & 96.40 & 98.28 & 98.94 & 99.47 & 98.96 & 97.31 & 98.37 & 98.56 & 90.99 & 88.32 & 96.46 & 94.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Classification performance of 5 gaits using right lower limb body section with KNN, NB, and DA. As we can visualize the results in Tables 11 and 12, in general, the performance of the five algorithms for the five metrics increases using the body region of the left lower limb, and DA is the best algorithm. Finally, we analyze the global performance of the algorithms for each metric, and Table 13 summarizes the average performance of the algorithms to classify the five gaits by body region. Considering average performance, for accuracy, the algorithm best evaluated in the pelvic and left regions is DA with 96.96 and 99.52%, respectively, while in the right region it is NB at 99.52%. The algorithm with the worst performance is KNN for the three cases. Regarding recall and specificity, the best performance was achieved in a similar way to the accuracy for the pelvic and left region; only for the left region, the algorithm with the best performance is NB with 98.59% and 99.70%, respectively. A similar performance occurs with the precision and F-measure, DA being the algorithm with the best performance for the first and third regions, and NB with 98. 79% and 98. 61%, respectively, for the region of the right lower limb.