# Hand Posture Recognition Using Skeletal Data and Distance Descriptor
## Experiments
### Comparison with Other Works  
Table 5 presents the comparison of the best results obtained by the method proposed in this paper and by the method from our previous work. The new method outperforms the previously proposed algorithm by 11.6%, which confirms the usefulness of the novel descriptor DD and the novel classifier NNOR.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Features** & **NNOR-Euc** & **NNOR-CB** \\ \hline PPD + DD (Euclidean) & 63.9 & 63.9 \\ PPD + DD (city block) & 56.7 & 58.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of leave-one-subject-out, 5-fold cross-validation tests obtained for Dataset 1 using nearest neighbor classifier with orientation restriction (NNOR).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Features** & **SVM-Lin** & **LD** & **TreeBag** & **10 NN-W** \\ \hline PPD + DD (Euclidean) & 99.7 & 82.5 & 100.0 & 100.0 \\ PPD + DD (city block) & 99.9 & 86.0 & 100.0 & 100.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of 10-fold cross-validation obtained for Dataset 1.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Features** & **SVM-Lin** & **LD** & **TreeBag** & **10 NN-W** \\ \hline PPD + DD (Euclidean) & 57.9 & 58.5 & 55.7 & 54.8 \\ PPD + DD (city block) & 55.7 & 57.0 & 55.8 & 49.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of leave-one-subject-out, 5-fold cross-validation tests obtained for the Dataset 1.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Features** & **NNOR-Euc** & **NNOR-CB** \\ \hline PPD + DD (Euclidean) & 63.9 & 63.9 \\ PPD + DD (city block) & 56.7 & 58.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of leave-one-subject-out, 5-fold cross-validation tests obtained for Dataset 1 using nearest neighbor classifier with orientation restriction (NNOR).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Features** & **SVM-Lin** & **LD** & **TreeBag** & **10 NN-W** \\ \hline PPD + DD (Euclidean) & 99.7 & 82.5 & 100.0 & 100.0 \\ PPD + DD (city block) & 99.9 & 86.0 & 100.0 & 100.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of 10-fold cross-validation obtained for Dataset 1.  
The authors know of only two publicly available sets of static hand skeletal data for which comparative analysis using our method is possible. They are Dataset 2 and Dataset 3, described in Section 4.1.  
Table 6 presents the comparison of the recognition rates obtained for Dataset 2 by our method (in two configurations) and by the methods proposed in two other works. The proposed algorithm outperforms other methods by more than 9%.  
Dataset 3 can be considered very challenging, since its authors [24] achieved a recognition rate of only 35.1% with skeletal data. Table 7 presents the comparison of the recognition rates obtained for this dataset by our method and the method proposed in other work.  
The experiments were performed using Matlab R2018b software with Classification Learner toolbox on a PC with an Intel Core i5-8300H, 2.3 GHz CPU, and 16 GB RAM. The average time of feature extraction is about 5 ms. The total recognition time (feature extraction and classification) does not exceed 70 ms. Therefore, the time delay measured from gesture execution to the predicted response of the program is barely noticeable by the user.