# WiFi-Based Gesture Recognition for Vehicular Infotainment System--An Integrated Approach
## 5 Experimentation and Evaluation
### Performance Evaluation  
First, we tested the usefulness of our extracted features, that is, mean (\(\mu\)), standard deviation (\(\sigma\)), median absolute deviation (MAD), maximum value (MAX), 25 th percentile (\(P_{25 th}\)) and 75 th percentile (\(P_{75 th}\)). Table 2 represents some prominent values of each calculated feature extracted for different gestures. One can notice that all features are distinctively different, indicating that these features can achieve high recognition accuracy. The recognition performance of the proposed method was observed by conducting extensive experiments. For simplicity, we use abbreviated terms for our proposed prototype, that is, MNN integrated with SRC as MNN-SRC, similarly KNN with SRC as KNN-SRC. We particularly selected a confusion matrix and recognition accuracy as metrics for performance evaluation. The occurrence of the actual gesture performed is represented by the column of the confusion matrix, whereas the occurrence of the gesture classified was represented by the rows. The confusion matrix in Figure 4 reveals the fact that our proposed method can recognize sixteen different gestures very accurately with an average accuracy of 91.4%, 90.6% and 88.7% for scenarios I, II and III respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Feature Type**} & \multicolumn{8}{c}{**Gesture Class**} \\ \cline{2-13}  & **SL** & **SR** & **HU** & **HD** & **FK** & **GB** & **PF** & **PB** & **RC** & **RA** & **SV** & **SX** & **SP** & **SN** & **HM** & **HR** \\ \hline \(\mu\) & 1.54 & 1.33 & 0.81 & 0.74 & 1.42 & 2.16 & 2.25 & 3.52 & 0.96 & 0.91 & 1.63 & 1.49 & 2.28 & 1.71 & 2.13 & 2.66 \\ \hline \(\sigma\) & 0.73 & 0.96 & 0.68 & 0.66 & 1.89 & 1.97 & 1.20 & 1.14 & 0.62 & 0.76 & 1.27 & 1.35 & 1.73 & 1.16 & 1.81 & 1.46 \\ \hline \(MAD\) & 0.48 & 0.64 & 0.45 & 0.43 & 1.37 & 1.31 & 0.83 & 0.76 & 0.41 & 0.50 & 0.84 & 0.90 & 1.15 & 0.73 & 1.21 & 0.97 \\ \hline \(MAX\) & 3.58 & 3.97 & 5.88 & 6.31 & 8.24 & 6.04 & 7.73 & 9.02 & 8.85 & 6.57 & 4.96 & 7.22 & 5.94 & 6.39 & 7.63 & 5.99 \\ \hline \(P_{25 th}\) & 0.69 & 0.60 & 0.27 & 0.37 & 0.58 & 0.82 & 0.98 & 1.22 & 0.35 & 0.44 & 0.64 & 0.51 & 0.77 & 0.65 & 0.95 & 0.91 \\ \hline \(P_{75 th}\) & 2.11 & 1.83 & 0.86 & 0.92 & 1.76 & 2.57 & 2.93 & 3.71 & 1.01 & 1.34 & 1.95 & 1.55 & 2.35 & 1.98 & 2.88 & 2.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Features test result. To ensure the reliability and efficacy of proposed framework, we analyzed the results by adopting different evaluation metrics including precision, recall and \(F_{1}\)-score. These evaluation metrics are presented as:  
Figure 4: Confusion matrix of activity recognition using our proposed algorithm. (**a**) Scenario-I. (**b**) Scenario-II. (**c**) Scenario-III. 1. Precision is defined as positive predictive value, mathematically described as: \[Precision=\frac{TP}{TP+FP}\] (38) where \(TP\) and \(FP\) are true positive and false positive respectively. True Positive (TP) is the probability that a model correctly predicts the positive class. Whereas, False Positive (FP) is the probability of that a model incorrectly predicts the positive class. 2. Recall is defined as the True Positive Rate (TPR) and measures the sensitivity of system as follows: \[Recall=\frac{TP}{TP+FN}\,\] (39) where \(FN\) is a false negative and is defined as the probability that the model incorrectly predicts the negative class. We will also evaluate our proposed method with False Negative Rate (FNR), defined as: \[FNR=\frac{FN}{TP+FN}\.\] (40)  
3. F-measure or \(F_{1}\)-score is defined as the weighted average of precision and recall, calculated as: \[F_{1}=2\times\frac{Precision\times Recall}{Precision+Recall}\.\] (41)  
Figure 5 shows the results related to precision, recall and \(F_{1}\)-score for all three scenarios, using our proposed scheme. Table 3 summarizes the performance of our MNN-SRC algorithm using TPR and FNR, for each gesture.