# The Role of Deep Learning and Gait Analysis in Parkinson's Disease: A Systematic Review
#### 3.3.2 Vision-Based Motion Capture System  
Table 2 shows the studies that employed video capture to study gait analysis in PD. As shown in Table 2, vision-based motion analysis were employed in 12 studies of this systematic review to extract information from sequential images in order to describe movement. The variables of gait considered in the following studies mainly revolved around the gait spatial parameters; the oscillatory movements of the upper limbs; and the movements in the sagittal plane of the lower limbs, such as knee flexion and ankle dorsiflexion. In particular, the maximum and minimum flexion and extension for the respective angle joints were computed. Figure 9 illustrates the DL-based approaches employed in the reviewed studies. In 2023, [PERSON] et al. exploited video data recorded during gait assessment to predict the severity of motor symptoms, which involved 74 PD patients [61]. The videos of all participants were divided into three groups based on the UPDRS Part III scores assigned by neurologists: mild, moderate, and severe. Specifically, they employed the ECO-Lite CNN architecture to learn spatio-temporal features from the videos. Initially, sixteen frames from each video were processed using a 2D network to generate 96 feature maps. Subsequently, these maps were fed into a 3D network to analyze the interactions between frames. The authors calculated the coefficient of determination (R2) between those scores to evaluate the model's goodness of fit. The R2 for the total UPDRS Part III score and the subscores of axial symptoms, i.e., bradykinesia, rigidity, and tremor, were 0.59, 0.77, 0.56, and 0.46, respectively. \begin{table}
\begin{tabular}{|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|} \hline
**Reference** & **Aim of the Study** & **Type of Dataset** & **Number of Subjects (Fe- male’M Male)** & **Mean Age of the Subjects** & **Acquisition Device** & **Features** & **Analytical Methods** & **Main Results** \\ \hline  & & & & & & & & The goodness of the model the coefficient of determination was evaluated. In particular, axial symptoms, bradykinesia, rigidity, and tremor. & Private & 44\(\backslash\)30 & 63.4 \(\pm\) 8.2 & Video camera & Spatio-temporal gait features & ECO-Lite CNN & The goodness of the model the coefficient of determination was evaluated. In particular, axial symptoms, bradykinesia, rigidity, and tremor. & 44\(\backslash\)30 & 63.4 \(\pm\) 8.2 & Video camera & Spatio-temporal gait features & ECO-Lite CNN & The goodness of the model the coefficient of determination was evaluated. In particular, axial symptoms, bradykinesia, rigidity, and tremor. & 44\(\backslash\)30 & 63.4 \(\pm\) 8.2 & Video camera & Spatio-temporal gait features & ECO-Lite CNN & The goodness of the model the coefficient of determination was evaluated. In particular, axial symptoms, bradykinesia, rigidity, and tremor. & 44\(\backslash\)30 & 63.4 \(\pm\) 8.2 & Video camera & Spatio-temporal gait features & OpenPose & Correlation coefficient 0.80 \\ \hline  & & & & & & & & & \\ [PERSON] et al., et al., et al., [62] & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\ \hline  & & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\ \hline  & & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\ \hline  & & & & & & & & & \\  & & & & & & & & \\ \hline  & & & & & & & & & \\ \end{tabular}
\end{table} 
Table 2: Studies employing Video Capture. Figure 9: The most common DL-based approaches used in video-based gait analysis are presented. The figure shows the names of the DL methods and the number of papers that employed each method. In the field of motion capture by videos, HPE has been widely used for the extraction of the body configuration in images or videos as a 3D markerless method against classical techniques and marker-based motion capture systems.