## 4 Results and Discussions
### Evaluation with Unseen Dataset  
Lastly, the trained BLSTM model was evaluated by the unencountered testing dataset. The graphs in Figure 9 show the testing dataset's predicted and actual joint angles. The testing was performed offline, where test data collected as part of the data collection was restructured into datasets and fed to the trained BLSTM model for prediction. The figures show some bias errors, which is to be expected given that each subject has a unique gait. However, this is a highly promising result for input data solely from a single IMU. Furthermore, a MATLAB Simulink(r) skeleton model was also created to visualize the estimated joint angles from the testing dataset. Figure 10 shows the comparison of the actual and simulated versions of the predicted four joint angles set. Both successive images were captured from a camera video and a simulated video. The video of the subject was taken during the data collection using a camera. Where the video of the skeleton was generated using a MATLAB built-in video recorder function. The subject was instructed to make momentary stops during the walking which we use later to synchronize both videos. An excellent pose estimation was achieved from only a single inertial sensor on the shank as can be referred from the figure. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **hPR\_x** & **knR\_x** & **hPL\_x** & **knL\_x** \\ \hline General & 5.76 & 6.01 & 6.81 & 6.16 \\ Personal & 2.65 & 3.79 & 2.73 & 3.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: MAE (in \({}^{\circ}\)) of the trained BLSTM network for personal and general pelvis datasets. Adapted with permission from Ref. [23]. 2022, IEEE. Figure 9: Ground-truth vs. estimated joint angles from a shank IMU using the trained BLSTM model. (**a**) right hip ext/flex angle; (**b**) right hip abd/add; (**c**) right knee ext/flex angle; (**d**) right ankle dorsi/plant; (**e**) left hip ext/flex angle; (**f**) left hip abd/add; (**g**) left knee ext/flex angle; (**h**) left ankle dorsi/plant. Furthermore, the model was evaluated using a cross-validation method. Table 5 shows the result of a 10-fold cross-validation over the testing dataset. The rows represent the MAE of the best model and the average of the MAE values of the 10 models of the cross-validator by joint angle. The best model was selected as the model with the smaller overall mean value of the joint-angle MAEs. The overall mean value of a model was calculated by taking the average of the MAE values of each joint. As a result, the overall mean value of the joint-angle MAEs ranges from 4.46\({}^{\circ}\) to 5.13\({}^{\circ}\) which indicates a similar result was obtained from the different dataset arrangement. Moreover, the results show a similar trend to Table 3 where the errors for the ankle joints and hip adduction/abduction are larger. To have an idea of how well our method can handle other datasets, an assessment with open-source datasets is essential. However, there are no relevant open-source datasets collected similar to ours. For reference, the results of the studies by the authors in [24; 25] are mentioned here, even though both studies employed different data-collection techniques and estimation methodologies. The authors in [24] gathered their data by giving their subjects instructions to walk on a wooden frame along a predetermined path. They claimed that for their 5 subjects, they were able to achieve a mean joint-angle error range of 5.35\({}^{\circ}\) to 12.3\({}^{\circ}\). The data-collection process by the authors in [25] was carried out in a 5 m-long indoor area. They have achieved a root mean square error range of 7.49\({}^{\circ}\) to 8.14\({}^{\circ}\) (using all features) and 6.19\({}^{\circ}\) to 7.0\({}^{\circ}\) (using selected features). Figure 10: Actual and predicted leg-joint comparison (for the four joint angles) through a graphical simulation.