## 4 Results
### Study Characteristics  
A summary of the findings of the 22 reviewed articles is given in Table 1. The information in each article was harmonized to facilitate comparability and analysis among studies. Figure 3: PRISMA flow diagram of literature search and selection process showing the number of studies identified, screened, and included in the review. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Reference** & **Study Design** & **Participants** & \begin{tabular}{l} **Device, Sensors, Number** \\ **(Device, Sensor), Body Location** \\ \end{tabular} & **Aim** & **Extraction Method** & **End Point** \\ \hline \multirow{3}{*}{[65]} & & \begin{tabular}{l} PwPD: \\ \(N=90\) (34 F; 56 M) \\ HIC \\ \end{tabular} & \begin{tabular}{l} Pressure insoles \\ VGRS sensors \\ N = (20 (34 F; 28 M) \\ N = (62 (34 F; 28 M) \\ \end{tabular} & Gait & \
\begin{tabular}{l} Features combined with \\ ML model (Random \\ Foot (8 each) \\ \end{tabular} &
\begin{tabular}{l} Gait features that impact the \\ predicted TUC scores are gait \\ speed-based features (peroentiles, \\ mean, and kurtosis), with \\ 84.8% accuracy. \\ \end{tabular} \\ \hline
[21] & Gait & \begin{tabular}{l} PwPD: \\ N=29 (12 F; 17 M) \\ HIC \\ N=27 (14 F; 13 M) \\ \end{tabular} & \begin{tabular}{l} IMU (Ovals by APDM\({}^{\text{B}}\)) \\ 3-axial accelerometer, 3-axial gyroscope \\ and 3-axial accelerometer, 3-axial gyroscope \\ and 3-axial accelerometer \\ N = (3, 3) \\ Root (1 (each)) and lower back \\ \end{tabular} & Classification & \
\begin{tabular}{l} Features combined with \\ ML model (Logistic \\ regression) \\ \end{tabular} &
\begin{tabular}{l} Turning and gait indicators \\ discriminate PwPD from HC (Turn \\ angle, swing time variability data \\ stride length with AUC = 0.87–0.89). \\ \end{tabular} \\ \hline
[66] & Gait & \begin{tabular}{l} PwPD: \\ N=29 (12 F; 17 M) \\ HIC \\ N=20 (8 F; 2 M) \\ \end{tabular} & \
\begin{tabular}{l} IMU (Ovals by APDM\({}^{\text{B}}\)) \\ 3-axial accelerometer, 3-axial gyroscope \\ and 3-axial magnetometer \\ N = (3, 3) \\ Root (1 (each)) and lower back. \\ \end{tabular} & Classification &
\begin{tabular}{l} Features combined with \\ ML model (Polynomial \\ length) could be used to classify \\ PwPD from HC, with AUC \textgreater{} 0.8. \\ \end{tabular} \\ \hline
[67] & Gait & \begin{tabular}{l} PwPD: \\ N=5 \\ HIC \\ N=5 \\ \end{tabular} & \begin{tabular}{l} IMU (Ovals) AXX\({}^{\text{B}}\)) \\ 3-axial accelerometer \\ N = (1, 1) \\ Lower back \\ \end{tabular} & \
\begin{tabular}{l} Classification \\ PwPD-HC \\ \end{tabular} & Features &
\begin{tabular}{l} The sample entropy of the gait \\ signal of PwPD are higher than \\ HC participants. \\ \end{tabular} \\ \hline
[68] & Gait & \begin{tabular}{l} PwPD: \\ Balance Task \\ Finger Tapping \\ (Mpower database) \\ \end{tabular} & \begin{tabular}{l} 3-axial accelerometer (gatt and balance) \\ N = 1057 (359 F; 698 M) \\ HC \\ N = (1, 2) \\ N = 543 (1014 F; 4329 M) \\ \end{tabular} & \
\begin{tabular}{l} Raw data + DL model \\ Convolutional neural \\ PwPD-HC \\ \end{tabular} &
\begin{tabular}{l} Tangping positions (Centered \\ trapping coordinates) are the most \\ relevant data (AUC = 0.935) for \\ PD detection. \\ \end{tabular} \\ \hline
[69] & Gait & \begin{tabular}{l} PwPD: \\ N=93 (35 F; 58 M) \\ (Physionet database) \\ N=73 (33 F; 40 M) \\ \end{tabular} & \begin{tabular}{l} Pressure insoles \\ VGRS sensors \\ N = (1, 16) \\ Root (8 each) \\ \end{tabular} & \
\begin{tabular}{l} Gait \\ monitoring \\ and \\ classification \\ PwPD-HC \\ \end{tabular} &
\begin{tabular}{l} Features combined with \\ ML model (Support vector \\ machine) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of included papers.