## 4 Methodology
### Dataset Description and Kinematic Parameter Selection  
Several papers have emphasized the necessity of a large and publicly available dataset to improve valuable metrics in gait research [1,7,24]. The motivation is for seamless performance comparison among developed algorithms; thus, benchmarking can easily be conducted and an agreement on significant parameters can be accelerated. With this in mind, a recently published gait analysis dataset [30] of 80 healthy and 106 subjects before and 6 months after THA is utilized in this research. HOA identification is based on American College of Rheumatology Criteria and verified with radiological assessment.  
Three-dimensional gait trajectories are captured via reflective markers and a vision-based system with eight optoelectronic cameras and floor-based sensors with two force plates utilized for the synchronized measurements. Thus, both kinematic and kinematic parameters are captured in the protocol. Ten trials are performed by each participant on a 6 m walkway. For brevity, demographic information of the volunteers is summarized in Table 2.  
In this study, trajectories from the reflective markers on the lower limbs are considered as shown Figure 6a. Different planar views, as shown in Figure 6b, of the computed kinematic trajectories are used, namely sagittal (x-axis), frontal/coronal (y-axis), and transverse (z-axis).  
\begin{table}
\begin{tabular}{c c c} \hline
**Demographic Information** & **HOA Patients** & **Control Subjects** \\ \hline Gender & 51 male and 55 female & 35 male and 45 female \\ Age & 66.9 \(\pm\) 9.4 years & 58.7 \(\pm\) 15.5 years \\ Height & 1.64 \(\pm\) 0.08 m & 1.66 \(\pm\) 0.08 m \\ Weight & 77.8 \(\pm\) 17.1 kg & 69.3 \(\pm\) 13.4 kg \\ \hline \end{tabular}
\end{table}
Table 2: Dataset demographics summary.  
Figure 5: Proposed approach.  
Figure 7: Aggregated joint angles of the affected limb.  
Through visual observations, HOA-afflicted gaits have larger variance compared to healthy gaits, with THA showing some variations as well. Subsequently, data augmentation strategies are considered to improve generalization by increasing the size of the dataset. Essentially, artificial data are generated to be included in the training process. Popular data augmentation strategies from the literature include translation, shifting, rotation, and noise addition. Closer inspection reveals that Gaussian noise addition is the only strategy applicable for gait sequences which can be explained by Equation (10) given below:  
\[x^{\prime(\in)}=\{x_{1}+\in_{1},x_{2}+\in_{2},\cdots,x_{N}+\in_{N}\} \tag{10}\]  
where \(\in\) is the Gaussian noise added for every event of the gait cycle. An example is shown in Figure 8, where \(\in=0.5\) is added on a randomly selected kinematic gait cycle from the dataset.  
For clarity, the overall pre-processing flow chart is shown in Figure 9. This is composed of three stages resulting in an input gait dataset that will be used for the DL model.  
In contrast to the method chosen by a recent study [24] that transforms gait into images, the proposed method utilizes the 1D gait sequence as input for the DL model. As a consequence, the DL model is designed from scratch and the training procedure is treated carefully given that there is no pre-trained network that can be used as a baseline.