## 3 Results and Discussion
### Training Performance of SVM and Comparison between the EMG and GRF Features  
The extracted features were set as inputs for the machine learning algorithm. Here, the GRF and EMG are used separately to train the model first, and then the combination of them is employed. The average and standard deviation of the overall accuracy, sensitivity and specificity of the different selected sets of features are shown in Table 5.  
The use of only the EMG or GRF features each has its drawback. The average accuracy, sensitivity and specificity of classifying the level ground using only the GRF features is not satisfied, as shown in Table 5, because the value of the GRF features of level ground is usually close to the other two terrains, as can be observed in Figure 6c,d, challenging the terrain condition classifier. It can be observed in Table 5 that when feeding only the EMG features into the SVM model, the overall accuracy, sensitivity, and specificity are all higher than those when using only the GRF features to train the model. The classifier achieved a better performance with the help of EMG, indicating that EMG signals better reflect the human body condition [28; 36; 37]. However, the sensitivity of downhill \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **GRF** & **EMG** & **GRF + EMG** \\ \hline \multicolumn{2}{c}{**Accuracy \(\pm\) SD**} & 80.96\% \(\pm\) 6.17\% & 89.93\% \(\pm\) 3.63\% & 96.76\% \(\pm\) 1.57\% \\ \hline \multirow{3}{*}{**SEN \(\pm\) SD**} & **Uphill** & 83.44\% \(\pm\) 5.47\% & 95.00\% \(\pm\) 3.28\% & 98.59\% \(\pm\) 1.15\% \\  & **Level ground** & 78.68\% \(\pm\) 7.36\% & 88.49\% \(\pm\) 4.62\% & 96.87\% \(\pm\) 1.62\% \\  & **Downhill** & 80.99\% \(\pm\) 10.25\% & 86.04\% \(\pm\) 6.97\% & 94.59\% \(\pm\) 3.24\% \\ \hline \multirow{3}{*}{**SPE \(\pm\) SD**} & **Uphill** & 78.84\% \(\pm\) 7.34\% & 86.31\% \(\pm\) 4.76\% & 95.77\% \(\pm\) 1.79\% \\  & **Level ground** & 84.07\% \(\pm\) 5.44\% & 92.46\% \(\pm\) 2.90\% & 97.99\% \(\pm\) 0.99\% \\ \cline{1-1}  & **Downhill** & 80.35\% \(\pm\) 5.94\% & 91.23\% \(\pm\) 3.73\% & 96.64\% \(\pm\) 2.19\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The average and standard deviation (SD) of the accuracy, sensitivity and specificity of the model using different sets of features. and level ground and the specificity of uphill are relatively lower, which means poor performance was achieved when distinguishing level ground and downhill; similar results were also observed in [22; 28]. This is because up to six features show no significant differences (\(p<0.05\)) between level ground and downhill, as shown in Figure 6, making the boundary between level ground and downhill ambiguous and hard to classify. When the model was trained with both the EMG and GRF features, its average accuracy reached 96.76%, which is sufficiently improved compared with using only the EMG or GRF features. Here, we present a confusion matrix for when both feature sets were fed into the model in Table 6. As Table 6 shows, all of the terrains have a classification accuracy over 94.59% and a standard deviation below 3.24%, indicating that a high and robust accuracy was achieved. Hence, the fusion of the EMG and GRF signals increased the performance of the SVM model, and better interpreted the change of the terrain because they complement each other [20; 32]. A comparison of our results with existing work is presented in Table 7, which shows that, firstly, our work reduced the total number of sensors. For example, in [32], the total number of sensors was nearly four times that in this work. Secondly, we utilized only two types of sensors, EMG and GRF, the results of which we compared to the work of [20], in which four different types of sensors were used. The fewer sensors that are applied, in terms of both number and type, the less complex and energy-consuming the circuit structure needs to be in order to obtain and process the signals; thus, our work resulted in reduced component costs, circuit complexity and energy consumption, while reaching a similar overall accuracy.