# WiFi-Based Gesture Recognition for Vehicular Infotainment System--An Integrated Approach
## 5 Experimentation and Evaluation
### Performance Evaluation
In general, the MNN-SRC algorithm has an average TPR of over 88.9%, with average FNR less than 11.1% for all three scenarios. In order to determine the efficacy of the integrated classification algorithm, some experiments were performed with each classifier separately and the results are presented in Figure 6. As can be seen, the average recognition accuracy of stand-alone MNN (Mean Nearest Neighbor) or SRC (Sparse Representation based Classification) method is less as compared to the integrated MNN-SRC algorithm. We have compared the performance of MNN-SRC with KNN-SRC as shown in Figure 7. It is obvious that the average recognition accuracy of MNN-SRC algorithm is higher as compared to KNN-SRC. Figure 8 ensures the reliability of our feature extraction method. The model was evaluated using state-of-the-art classifiers including SVM, NB, KNN, MNN and SRC algorithms. One can notice that all classifiers show a satisfactory recognition results. However, our proposed MNN-SRC classification model outperforms other classification algorithms. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline
**Experiment** & **Gesture** & **SL** & **SR** & **HU** & **HD** & **FK** & **GB** & **PF** & **PB** & **RC** & **RA** & **SV** & **SX** & **SP** & **SN** & **HM** & **HR** \\ \hline \multirow{2}{*}{Scenario-I} & TPR (\%) & 92.7 & 91.0 & 95.9 & 96.9 & 89.0 & 89.1 & 87.4 & 85.6 & 89.3 & 90.1 & 93.8 & 88.2 & 87.6 & 96.8 & 95.0 & 96.0 \\ \cline{2-13}  & FNR (\%) & 7.3 & 9.0 & 4.1 & 3.1 & 11.0 & 10.9 & 12.6 & 14.4 & 10.7 & 9.9 & 6.2 & 11.8 & 12.4 & 3.2 & 5.0 & 4.0 \\ \hline \multirow{2}{*}{Scenario-II} & TPR (\%) & 94.6 & 95.8 & 96.7 & 93.0 & 89.7 & 89.1 & 85.0 & 86.4 & 86.9 & 87.6 & 88.2 & 90.0 & 87.5 & 93.7 & 93.1 & 93.9 \\ \cline{2-13}  & FNR (\%) & 5.4 & 4.2 & 3.3 & 7.0 & 10.3 & 10.9 & 15.0 & 13.6 & 13.1 & 12.4 & 11.8 & 10.0 & 12.5 & 6.3 & 6.9 & 6.1 \\ \hline \multirow{2}{*}{Scenario-III} & TPR (\%) & 92.5 & 92.7 & 93.6 & 92.8 & 86.7 & 86.5 & 86.7 & 82.9 & 85.1 & 84.6 & 89.9 & 88.0 & 80.4 & 91.6 & 94.8 & 93.0 \\ \cline{2-13}  & FNR (\%) & 7.5 & 7.3 & 6.4 & 7.2 & 13.3 & 13.5 & 13.3 & 17.1 & 14.9 & 15.4 & 10.1 & 12.0 & 19.6 & 8.4 & 5.2 & 7.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance evaluation for each gesture. Figure 5: Precision, recall and F-1 score. (**a**) Scenario-I. (**b**) Scenario-II. (**c**) Scenario-III. Figure 6: Comparison of accuracy using SRC and MNN separately. (**a**) Scenario-I. (**b**) Scenario-II. (**c**) Scenario-III. Figure 7: Comparison of accuracy using KNN-SRC and MNN-SRC. **(a)** Scenario-I. **(b)** Scenario-II. **(c)** Scenario-III. Figure 8: Comparison of accuracy using state-of-the-art classifiers. (**a**) Scenario-I. (**b**) Scenario-II. (**c**) Scenario-III. The overall performance comparison of particular state-of-the-art classifiers is illustrated in Table 4. From the experimental results, it can be concluded that our proposed MNN-SRC method is much better as compared to KNN-SRC or stand-alone conventional classification methods including MNN, KNN, SRC, SVM and NB. From Figure 9, we can observe that the proposed system could achieve reasonable performance even using only the amplitude or phase information. However, the recognition accuracy will be significantly better when we are combining both the amplitude and phase information. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Experiment**} & \multicolumn{6}{c}{**Average Recognition Accuracy (\%)**} \\ \cline{2-7}  & **NB** & **SVM** & **KNN** & **MNN** & **SRC** & **KNN-SRC** & **MNN-SRC** \\ \hline Scenario-I & 85.9 & 88.5 & 87.1 & 87.5 & 89.5 & 90.7 & 91.4 \\ \hline Scenario-II & 84.1 & 86.6 & 85.4 & 86.2 & 88 & 90.1 & 90.6 \\ \hline Scenario-III & 82.5 & 85.1 & 84.3 & 84.5 & 86.6 & 88.1 & 88.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of accuracy with state-of-the-art classification methods. Figure 9: _Cont._  
To study the impact of nearest neighbors, we performed experiments with varying \(K\) values.