# The Role of Deep Learning and Gait Analysis in Parkinson's Disease: A Systematic Review
\begin{tabular}{c} Develop of a robust and \\ innovative graphical adaptive \\ network based on the frequency \\ domain (RdEdG) to identify PD \\ through gait information, \\ specifically vertical ground \\ reaction force signals recorded by \\ foot sensors. \\ \end{tabular}  } & \multirow{9}{*}{68'98} & \multirow{9}{*}{63.3} & \multirow{9}{*}{117\(\sim\)70 y, 499-70 yo} & \multirow{9}{*}{8 ground force sensor on each} & Time and frequency & \multirow{9}{*}{RFdGAD} & \multirow{9}{*}{Accuracy \(>\) 75\%, F1 \textgreater{} 70\%} \\  & & & & & & & \\ \hline \multirow{9}{*}{[PERSON] et al., 2022, [57]} & \multirow{9}{*}{ \begin{tabular}{c} Develop a hybrid DL model to \\ predict the severity of PD. In this \\ combined DL approach, the \\ temporal and frequency features \\ of ground reaction force sensors \\ are converted and used as input \\ for the CNN + LWRF architecture. \\ \end{tabular}  } & \multirow{9}{*}{68'98} & \multirow{9}{*}{63.3} & \multirow{9}{*}{8 ground force sensor on each} & Time and frequency & \multirow{9}{*}{CNN and 1 LWRF} & \multirow{9}{*}{ \begin{tabular}{c} Accuracy: 99.5\%, \\ Sensitivity: 98.7\%, \\ Specificity: 99.1\% \\ Specificity: 99.1\% \\ \end{tabular}  } \\  & & & & & & & & \\ \hline \multirow{9}{*}{[PERSON] et al., 2022, [57]} & \multirow{9}{*}{ \begin{tabular}{c} Develop an innovative algorithm \\ for detecting and classifying the \\ severity of PD using DL \\ approaches and relying on \\ signals of vertical ground \\ reaction force. Various types of \\ CNNs were employed as \\ classifiers. \\ \end{tabular}  } & \multirow{9}{*}{ \begin{tabular}{c} Public (Ga, \\ Ju, and Si \\ datasets) \\ \end{tabular}  } & \multirow{9}{*}{68'98} & \multirow{9}{*}{63.3} & \multirow{9}{*}{8 ground force sensor on each} & Time-frequency & \multirow{9}{*}{ResNet-50, 101} & \multirow{9}{*}{ \begin{tabular}{c} Classification: accuracy \\ classification: accuracy \\ classification: accuracy \\ 99.11%, 99.01%, 98.56% \\ Sensitivity: 99.77\% \\ 98.16%, 98.83%, 98.76\% \\ CNN, AlexNet, \\ ResNet-50, \\ ResNet-101, \\ \end{tabular}  } & \multirow{9}{*}{ \begin{tabular}{c} Public (Ga, \\ Lu, Si \\ datasets); two-class classification: accuracy \\ classification: accuracy \\ 99.11%, 99.01%, 98.56\% \\ Sensitivity: 99.77\% \\ 98.16%, 98.83%, 98.96\% \\ 98.83%, 98.96\% \\ ResNet-50, \\ ResNet-101, \\ \end{tabular}  } & \multirow{9}{*}{\ \begin{tabular}{c} Public (Ga, \\ Lu, Si \\ datasets); two-class classification: accuracy \\ classification: accuracy \\ 99.11%, 99.01%, 98.56\% \\ Sensitivity: 99.77\% \\ 98.16%, 98.83\%, 98.96\% \\ 98.83%, 98.96\% \\ AUC 99\% (Ga, Lu, Si datasets); \\ ResNet-101, \\ \end{tabular}  } & \multirow{9}{*}{ \begin{tabular}{c} Predict PD gains (Ga \\ dataset: accuracy \\ 99.31, sensitivity \\ 99.35\%, specificity \\ 99.23\%, classify PD \\ details with different \\ H&K scores (Si \\ dataset: accuracy \\ 99.01\% \\ \end{tabular}  } \\ \cline{1-1}  & & & & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-  
In 2023, [PERSON] et al. used smartphone accelerometers and gyroscopes to collect data from 456 subjects (152 PD and 304 HC subjects). The objective was to determine whether PD patients took their medication by utilizing the DeepaMed CNN model [46]. The study findings suggest that medication non-adherence among PD patients can be reliably predicted through the smartphone-based monitoring of motor symptoms. In 2021, [PERSON] et al. adopted samples for steps and samples for strides recorded with four sensors: an AX3 (Company name: Axivity, address: Newcastle upon Tyne, United Kingdom (UK)) on a wrist, a GENEActiv (Company name: Activinsights Ltd, address: Kimbolton, Cambridgeshire, United Kingdom (UK)) on the other wrist, a AX3 attached with hypoallergenic tape to the lower back, and a GENEActiv sensor strapped around the shin. For the experiments, 12 subjects (6 PD and 6 HC subjects) performed slow gait, normal gait, fast gait, and timed up-and-go (TUG) tests [49].