# Impact of Load Variation on the Accuracy of Gait Recognition from Surface EMG Signals
## 3 Results
### Accuracies of Five Gait Phases under Different Loads  
Four different BPNN models were trained at each speed. As we can see in Table 1, the total average accuracies of gait recognition (total Avg) under the four loads at three speeds were very different. The highest value was 94.25% at V5, followed by 92.55% at V7, and the minimum value was only 88.61% at V3. There was also a significant difference in load when the highest accuracy of gait recognition appeared at each speed. At V3, the highest accuracy of gait recognition (91.94%) occurred when the load was L30. At V5, the highest value (97.56%) appeared when the load was L20, which was the highest among the four loads and three speeds. When the speed was V7, the highest value (94.62%) appeared when the load was L0 (no load). At the same time, when the load was increased to L40, the gait recognition accuracy was the lowest among the three speeds (83.05% at V3, 91.18% at V5, and 89.7% at V7, respectively).  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Speed** & \multicolumn{4}{c}{**V3**} & \multicolumn{4}{c}{**V5**} & \multicolumn{4}{c}{**V7**} \\ \hline
**Load** & **L0** & **L20** & **L30** & **L40** & **L0** & **L20** & **L30** & **L40** & **0** & **L20** & **L30** & **L40** \\ \hline Pre-stance & 89.25 & 96.00 & 84.81 & 80.89 & 97.69 & 96.61 & 86.33 & 83.57 & 92.21 & 90.35 & 94.39 & 89.51 \\ Mid-stance & 98.71 & 90.40 & 98.25 & 87.18 & 94.67 & 99.14 & 99.49 & 98.17 & 99.35 & 99.03 & 98.17 & 98.52 \\ Terminal stance & 91.53 & 95.18 & 95.65 & 88.47 & 86.84 & 95.27 & 95.50 & 91.71 & 89.57 & 88.22 & 86.07 & 88.51 \\ Pre-swing & 80.11 & 72.69 & 88.00 & 86.61 & 97.00 & 98.14 & 92.71 & 95.58 & 94.57 & 92.24 & 89.86 & 94.29 \\ Terminal swing & 94.55 & 88.73 & 92.99 & 72.10 & 98.90 & 98.63 & 92.07 & 86.87 & 97.42 & 98.41 & 92.71 & 80.18 \\ Accuracy & 90.83 & 88.6 & **91.94** & **83.05** & 95.02 & **97.56** & 93.22 & **91.18** & **94.62** & 93.65 & 92.24 & **89.70** \\ \hline Total Avg & \multicolumn{4}{c}{88.61} & \multicolumn{4}{c}{94.25} & \multicolumn{4}{c}{92.55} \\ \hline \hline \end{tabular}  
* The bold are the minimum and the maximum of accuracy of gait recognition among four loads at each speed.  
\end{table}
Table 1: Accuracy of recognition of the five gait phases (%), the accuracy of the gait recognition (%) under each load and speed, and the total average accuracy of the gait recognition (Total Avg) (%) among the four loads under each speed.  
Figure 3: Raw sEMG, _RMS_, and _iEMG_ for the four muscles from one subject, with L20 at V5.  
UNIANOVA indicated that the load variation had significant effects on the accuracy of gait recognition (\(p<0.05\)) when the loads were in the range of L0, L20, L30, and L40, but no significant impact was found when the loads are in the range of L0, L20, and L30 (\(p>0.05\)). Furthermore, the LSD post hoc test revealed that there was a significant difference between the L40 load and the other three kinds of loads (L0, L20, L30), but no significant differences were found among the three loads (L0, L20, L30).  
As a comparison, the SVM and k-NN algorithms were employed to deal with the same data. In general, the results presented in Figure 4 revealed that the gait recognition accuracy was the highest for the BPNN, followed by k-NN, and lowest for the SVM. Moreover, a similar tendency in gait recognition accuracy was found in the results of the three classification algorithms for all trials. Then, the gait recognition accuracy was analyzed by UNIANOVA, and the same conclusion that the load variation had a significant impact on the accuracy of the gait recognition was obtained (k-NN: \(p<0.05\); SVM: \(p<0.05\)).