## 4 Results and Discussions
### Network Performance with the Different Datasets  
The number of combinations of the datasets and the neural networks is large. Therefore, to reduce the computational time, the best-performing network was first selected by training four of the networks using solely PID datasets. Next, the selected network will be trained using the four datasets namely: PID, thigh inertial dataset (TID), shank inertial dataset (SID), and foot inertial dataset (FID).  
The training performance for the PID with the BLSTM network is depicted in Figure 8. This loss is computed after every training step during the training process. As can be seen from the graph, the network learned the features well in the first 20 epochs without any overfitting or underfitting problems. Even though there is a gap between the two graphs, the difference is small enough to be deemed as an overfitting model. All the networks employed the Huber loss function and Adam optimizer. It can be seen from Table 1 that the performance of the BLSTM and LSTM models exceeded the other two models. This is because both recurrent networks are excellent at learning temporal features included  
Figure 7: BLSTM network for the PID dataset.  
in the time-series data. Their close mean absolute errors (MAEs) for the PID indicate the significance of temporal information for human lower-limb pose estimation problem over spatial information. Because CNN and WNN are better at extracting and learning spatial features than recurrent networks. However, their results are inferior compared to LSTM and BLSTM. Their results, which are shown in Table 1, were acquired by testing the trained models of the four neural networks with unseen testing dataset types.  
From the total average, BLSTM outperformed the other models in predicting the joint angle in all cases. Next to BLSTM, LSTM and CNN come, respectively, due to their overall performance as can be seen from the average columns. When the input dimension increased, especially in PID + bFID, spatial features can be extracted from the two datasets making it easier for WNN and CNN networks. One observation from Table 1 is that the accuracy for the knee joints significantly increased when the bFID data were included in the input. This is because new knee joint information is obtained from the bFID dataset. With PID, WNN struggled to perform well. Because WNNs employ classical sigmoid activations along with randomly initialized weights. During training, this leads to the network converging at a local minimum point. That is why WNNs did not perform well in the training.  
Adding biometric information and feet inertial data to the pelvis inertial data have improved the total average prediction accuracy of BLSTM by 4.12% and 33%, respectively. This proves that the way we walk is influenced by our biometric information. However, the bFID supplements the PID by adding more kinematic information about the foot which is far from the pelvis sensor.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Networks**} & \multirow{2}{*}{
\begin{tabular}{c} **Networks** \\ **Parameter** \\ **(PID)** \\ \end{tabular} } & \multicolumn{4}{c}{**PID**} & \multicolumn{4}{c}{**FID + bFID**} & \multicolumn{4}{c}{**FID + bFID**} \\ \cline{3-14}  &