## 5 Results and Discussion
### Comparative Analysis  
Regarding research on HOA classification through ML techniques, two other related studies' reports were compared to the results of the proposed architecture: (1) SVM classifier by [PERSON] et al. [23] and (2) image-based CNN classifier via transfer learning method by [PERSON] et al. [24]. While both investigations acquired datasets from the same laboratory, the former utilized a smaller and unpublished dataset, while the latter used the same published dataset. Figure 10 shows the results of the common kinematic parameters from these studies, and it is notable that the proposed HLSTM-CNN is in agreement that the hip sagittal angle is the most discriminating parameter for HOA classification. Both the image-based CNN and the HLSTM-CNN models recorded an outstanding 97% accuracy on this kinematic parameter, while the SVM model only recorded 85% accuracy. The results of the SVM model for the other kinematic parameters are decent for ML standards, which are slightly below 75%.  
With the proposed architecture, for knee sagittal and FPA front angles, there is a considerable uptick in accuracy with improvements of 3.8% and 5.2%, respectively, as compared to the image-based CNN model. Even with the use of raw gaits as inputs  
Figure 10: Comparison from the literature: SVM [23], and CNN (transfer learning) [24].  
\begin{table}
\begin{tabular}{c r r r r} \hline
**Kinematic Parameter** & **Accuracy** & **Sensitivity** & **Specificity** & **G-Mean** \\ \hline front ankle & 92.31 & 93.15 & 91.80 & 92.47 \\ front knee & 96.34 & 97.18 & 95.83 & 96.51 \\ front FPA & 95.03 & 94.55 & 95.28 & 94.91 \\ front hip & 92.89 & 93.15 & 92.74 & 92.95 \\ front pelvis & 93.75 & 96 & 92.48 & 94.22 \\ sagittal ankle & 87.96 & 85.33 & 89.66 & 87.47 \\ sagittal knee & 96.76 & 100 & 95.16 & 97.55 \\ sagittal FPA & 75.86 & 82.98 & 73.72 & 78.21 \\ sagittal hip & 97.22 & 100 & 95.73 & 97.84 \\ transverse pelvis & 84.73 & 83.12 & 85.71 & 84.41 \\ \hline \end{tabular}
\end{table}
Table 5: Performance metrics summary for healthy and HOA gait classification.  
to a model, there is no reduction in the performance, and has even improved results. Thus, computationally expensive scalogram image transformation is avoided without noticeable disadvantages.  
The ranking of importance for the kinematic parameters is similar as with the previous studies, with the sagittal angle of the FPA as the least discriminatory in the gait classification problem. The accuracy of HLSTM-CNN stands at 75.8%, which is a relatively low result, but the sagittal angle of the FPA has not been considered in the literature for HOA classification.  
Additionally, DL methods, both image-based CNN and HLSTM-CNN, are exceptionally superior to the SVM, giving results with at least a 12% accuracy improvement on the hip and knee sagittal angles. Besides this advantage, the manual handcrafting of data for feature extraction is eliminated in the process; thus, automation for classification is a possibility with less intervention from users.  
On the other hand, [PERSON] et al. [25] differentiated gaits between healthy patients and patients 2 weeks after THA operation utilizing IMU sensors. It is an important distinction from this study's dataset of using gaits 6 months after THA. It is shown that the resulting kinematic accuracy is 97%. Thus, there is a discriminating feature between healthy and THA subjects. Misclassification could provide information into the effects of the surgery itself that can lead to better rehabilitation.