# Vision-Based Hand Rotation Recognition Technique with Ground-Truth Dataset
## 6 Discussion  
In this paper, we define measurement behavior for extraction technology and propose an image-processing-technology-based measurement system for novel behavior indicators needed for early screening of cognitive-ability-based diseases. The measurement motion of hand rotation places less physical burden on the subject and enables periodic examination because of its low learning effect. The system uses a web camera to minimize psychological barriers that may arise during cognitive ability screening tests through unfamiliar instruments. In addition, to verify the validity of the technology, we formulated hypotheses for situations that may occur during hand rotation and established a hand rotation ground-truth dataset for experimentation. As a result of the experiment, the number of rotations and angle of rotation indicators, excluding time of rotation, showed average accuracies of \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Value (N)** & **Number of Rotations** & **Angle of Rotation** & **Time of Rotation** \\ \hline \multirow{3}{*}{Hand} & Left (270) & 99.17 (3.64)\% & 93.33 (6.93)\% & 71.10 (16.07)\% \\ \cline{2-5}  & Right (270) & 99.26 (5.47)\% & 90.48 (6.75)\% & 66.26 (16.79)\% \\ \hline \multirow{3}{*}{Number of rotations} & 10 (486) & 99.26 (4.81)\% & 91.44 (7.18)\% & 67.49 (17.04)\% \\ \cline{2-5}  & 15 (54) & 99.01 (2.72)\% & 96.08 (2.18)\% & 79.39 (3.62)\% \\ \hline \multirow{3}{*}{TOK} & 0 (360) & 99.11 (5.41)\% & 91.72 (7.80)\% & 72.94 (14.28)\% \\ \cline{2-5}  & 50 (126) & 99.26 (2.79)\% & 93.39 (4.78)\% & 64.52 (18.55)\% \\ \cline{2-5}  & 100 (54) & 99.81 (1.36)\% & 89.68 (4.37)\% & 49.97 (9.34)\% \\ \hline \multirow{3}{*}{TBR} & 0 (324) & 99.26 (5.17)\% & 91.98 (7.78)\% & 75.33 (11.00)\% \\ \cline{2-5}  & 30 (54) & 99.81 (1.36)\% & 89.68 (4.37)\% & 49.97 (9.34)\% \\ \cline{2-5}  & 50 (162) & 98.93 (4.21)\% & 92.50 (5.77)\% & 61.60 (19.84)\% \\ \hline \multirow{3}{*}{TCA} & 0 (324) & 99.57 (2.91)\% & 92.33 (5.72)\% & 64.64 (15.98)\% \\ \cline{2-5}  & 30 (54) & 99.81 (1.36)\% & 89.68 (4.37)\% & 49.97 (9.34)\% \\ \cline{2-5}  & 50 (162) & 98.31 (7.30)\% & 91.81 (9.44)\% & 82.99 (4.94)\% \\ \hline \multirow{3}{*}{CAA} & \(-4.84^{\circ}\) (9) & 100.00 (0.00)\% & 95.16 (3.65)\% & 63.91 (0.71)\% \\ \cline{2-5}  & \(-4.4^{\circ}\) (24) & 99.72 (1.36)\% & 97.03 (2.30)\% & 83.08 (2.15)\% \\ \cline{2-5}  & \(-3.08^{\circ}\) (9) & 100.00 (0.00)\% & 85.46 (2.60)\% & 54.69 (2.15)\% \\ \cline{2-5}  & \(-2.6^{\circ}\) (24) & 100.00 (0.00)\% & 93.98 (1.71)\% & 77.95 (2.37)\% \\ \cline{2-5}  & \(-1.32^{\circ}\) (9) & 100.00 (0.00)\% & 89.19 (1.06)\% & 54.45 (1.90)\% \\ \cline{2-5}  & \(-0.88^{\circ}\) (24) & 99.72 (1.36)\% & 94.92 (2.36)\% & 78.08 (3.23)\% \\ \cline{2-5}  & \(0^{\circ}\) (342) & 98.98 (5.66)\% & 90.94 (7.98)\% & 69.42 (17.72)\% \\ \cline{2-5}  & \(0.88^{\circ}\) (24) & 100.00 (0.00)\% & 94.25 (4.12)\% & 71.82 (4.59)\% \\ \cline{2-5}  & \(1.32^{\circ}\) (9) & 100.00 (0.00)\% & 87.42 (1.04)\% & 45.59 (2.60)\% \\ \cline{2-5}  & \(2.6^{\circ}\)(24) & 99.44 (1.88)\% & 94.30 (4.51)\% & 68.58 (9.48)\% \\ \cline{2-5}  & \(3.08^{\circ}\) (9) & 100.00 (0.00)\% & 88.11 (1.15)\% & 42.13 (3.43)\% \\ \cline{2-5}  & \(4.4^{\circ}\) (24) & 98.47 (3.68)\% & 95.70 (3.05)\% & 64.00 (14.21)\% \\ \hline \multirow{3}{*}{ACA} & 4.84\({}^{\circ}\) (9) & 98.89 (3.33)\% & 92.75 (5.47)\% & 39.04 (7.71)\% \\ \cline{2-5}  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \end{tabular}
\end{table} 
Table 4: Accuracy results between ground-truth dataset and predicted values by variable. more than 90%, and the time of rotation indicator also has a convincing average accuracy considering that the input image was measured every 0.03 s on 30 FPS video.