# Pose-Based Gait Analysis for Diagnosis of Parkinson's Disease
## 4 Conclusions and Future Work  
In this study, we proposed gait features extracted from videos using markerless motion capture techniques for PD detection. First, we performed HPE on the subjects using AlphaPose. The results of the experiments performed in this study suggest that PD can be predicted in videos using markerless motion capture with up to 93% accuracy from arbitrary views, 95% in the front view, and 97% accuracy in the side view. This is most likely because most PD-related gait features can be more easily observed in the side view of gait. Most of the extracted features show more discriminative ability in the side view, especially gait speed, step length, and arm swing. As expected, the five features common to the front and side views are selected as important for all views, namely, arm steadiness, arm swing, step height, step length, and gait speed. We propose that these five features can be used for PD prediction in arbitrary views.  
Although this study has used a modest-sized self-collected dataset, the results show the potential of using markerless motion capture for the early detection of [PERSON]'s disease. We note that vision-based approaches can be based only on the detection of  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**View** & **Train/Test** & **Features** & **Smooth** & **Acc** & \multicolumn{3}{c}{**Healthy**} & \multicolumn{3}{c}{**Parkinsonian**} \\  & & & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{3}{*}{Front} & 8 & \(\times\) & 95\% & 0.95 & 0.95 & 0.95 & 0.95 & 0.95 & 0.95 \\  & 505/169 & 8 & ✓ & 95\% & 0.95 & 0.95 & 0.95 & 0.95 & 0.95 \\  & 5 & \(\times\) & 95\% & 0.95 & 0.95 & 0.95 & 0.95 & 0.95 & 0.95 \\ \hline \multirow{3}{*}{Side} & 225/75 & 8 & \(\times\) & 97\% & 1.00 & 0.92 & 0.96 & 0.96 & 1.00 & 0.98 \\  & 7 & \(\times\) & 97\% & 1.00 & 0.92 & 0.96 & 0.96 & 1.00 & 0.98 \\ \hline \multirow{3}{*}{All} & 8 & \(\times\) & 92\% & 0.88 & 0.92 & 0.90 & 0.94 & 0.92 & 0.93 \\  & 8 & ✓ & 93\% & 0.90 & 0.94 & 0.92 & 0.96 & 0.93 & 0.94 \\ \cline{1-1}  & 5 & \(\times\) & 93\% & 0.88 & 0.94 & 0.91 & 0.96 & 0.92 & 0.94 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results summary of PD prediction.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Feature** & \multicolumn{3}{c}{**Feature Ranks by View**} & \multicolumn{3}{c}{**Selected Features by View**} \\  & **Front** & **Side** & **All** & **Average** & **Front** & **Side** & **All** \\ \hline \multirow{3}{*}{
\begin{tabular}{l} posture \\ arm steadiness \\ arm swing \\ arm swing \\ arm swing symmetry \\ step height \\ step length \\ \\ \end{tabular} } & 7 & 6 & 7 & 7 & \(\times\) & ✓ & \(\times\) \\  & 4 & 7 & 6 & 6 & ✓ & ✓ & ✓ \\  & 4 & 7 & 6 & 6 & \(\checkmark\) & ✓ & ✓ \\  & 4 & 7 & 6 & 6 & \(\checkmark\) & \(\times\) & \(\times\) \\  & 4 perceivable gait impairments. Hence, further clinical investigations may be required to determine if a patient suffers from PD or gait impairment due to ageing or other conditions. Notwithstanding, the main advantage of this approach is its ease of use, potential reduction of cost, reduction of waiting times, and reduction of burden on trained clinicians. Future research will focus on investigating more features to detect different phases of [PERSON]'s disease progression.  
Conceptualization, T.C. and M.K.O.G.; methodology, T.S.O.; software, T.B.A.; validation, B.E.; formal analysis, B.P.; data curation, T.C. and T.B.A.; writing--original draft preparation, T.B.A.; writing--review and editing, T.C., T.S.O., M.K.O.G., B.E. and B.P.; supervision, T.C.; funding acquisition, T.C. All authors have read and agreed to the published version of the manuscript.  
This project is funded by a Multimedia University and Universitas Tekom Joint research grant (MMUE/210063).  
The AlphaPose body keypoints will be shared on request.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
2D 2-dimensional  
PD [PERSON]'s disease  
HPE Human pose estimation