## 5 Conclusions  
In this paper, we proposed a graph convolutional neural network that can capture the characteristics and relationships of skeleton input data in the spatial and temporal dimensions. Our method takes advantage of the structural information possessed by the input skeleton human pose which is extracted using OpenPose. In the method, spatial and temporal features of the input are represented in a single adjacency matrix that helps to apply graph convolution in both dimensions. As a result, the method captures the main features of the motion of the patient that contribute to predicting the gait parameter. Our approach has experimented on the cerebral palsy disorder and it outperformed the  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Part** & **Layer Type** & **Layer** & **Number of Unit** & **Kernel Size** & **Dropout** \\ \hline i & Graph Convolutional & 1 & 8 & - & 0 \\  & Graph Convolutional & 1 & 16 & - & 0 \\ \hline ii & Convolutional & 1 & 8 & 7 & 0 \\  & Convolutional & 1 & 16 & 7 & 0 \\ \hline  & Concatenation i and ii & - & 16 & & - \\ \hline iii & Convolutional & 1 & 8 & 8 & 0.5 \\  & Maxpooling & 2 & 16 & 3 & 0.5 \\  & Convolutional & 2 & 8 & 8 & 0.5 \\  & Flatten & - & 16 & - & 0.5 \\  & FC(Fully Connected NN) & 1 & 96 & - & 0.5 \\  & FC(Fully Connected NN) & 1 & 20 & - & 0.5 \\  & Output & - & 1 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Layer descriptions of the proposed method: the first part of the graph convolutional network is composed of two graph convolutional layers, the second part is 1D convolutional for hand engineered input data and the third part of the layers comprise 1D convolutional, maxpooling, and fully connected layer for the concatenated input from part i and ii.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Correlation for only GCNN** & **Correlation for GCNN** \\ \hline Walking speed (m/s) & 0.651 (0.618–0.694) & 0.791 (0.742–0.853) \\ Gait Deviation Index & 0.580 (0.542–0.624) & 0.792 (0.750–0.822) \\ Cadence (strides/s) & 0.632 (0.584–0.653) & 0.785 (0.756–0.802) \\ Knee flexion (degrees) & 0.681 (0.643–0.741) & 0.832 (0.752–0.867) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of our proposed graph convolutional neural network (GCNN) depicted in Figures 3 and 4. Here, we observe the method that takes the hand crafted inputs (Figure 4) has a better result.  
state-of-the-art method on the dataset that was processed by Gillette Speciality Healthcare. Our method predicted clinically apparent motion metrics from an ordinary video of patients with the cerebral palsy disorder. The method helps the clinicians to address the symptoms of neurological and musculoskeletal disorders without placing reflective markers on patients' anatomical landmarks which is very expensive and takes a lot of effort and time to diagnosis the patients. In addition, the method achieved the result with fewer parameters, faster training, and earlier convergence. In future work, we will apply the proposed method for other types of musculoskeletal and neurological disorders.  
[PERSON] and [PERSON] proposed conceptualization, [PERSON] performed the experiment and wrote the first draft. [PERSON], [PERSON] and [PERSON] assisted with revisions and improvements. The work was performed under the supervision and guidance of [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
As described in this paper, the data prepared by Gillette Speciality [5] Healthcare is processed by using the OpenPose algorithm [12]. Due to privacy of the patients, the video data are not publicly available. However, the preprocessed data are publicly available at [11][PERSON]  
The statements made herein are solely the responsibility of the authors. The authors would like to thank the University of Arkansas and Neuromechanics of Mobility Lab, West Virginia University.  
The authors declare no conflict of interest.