# Reducing the Number of Sensors in the Data Glove for Recognition of Static Hand Gestures
## 4 Results  
Of the many existing sign languages, it was decided to conduct the research based on the Polish Sign Language. In the case of static characters, it is not important which hand they are made with. Therefore, for practical reasons, the left hand was chosen. All classification processes were performed using a five-fold cross-validation technique. For the standard \(k\)-NN algorithm methods, the k parameter was set to 1. For the \(k\)-NN in Random Spaces (\(k\)-NNiRS) method, the number of subspaces was chosen automatically by the software and was equal to about half of the total number of data dimensions. The goal of the experiment was to investigate the possibility of reducing the number of sensors while losing as little gesture recognition performance as possible. For this purpose, the Maximum Relevance-Minimum Redundancy (MRMR) method was used. It is an iterative method of determining the most relevant features of the dataset under study. The score for each variable is determined as the quotient of significance and redundancy against previously tested variables. Relevance is calculated using  
Figure 5: The methodology flowchart. Figure 6: Distribution of measurements data. F-statistic between the feature and the expected class, while redundancy is calculated as the average Pearson correlation between the variable under study and the variables checked in previous iterations [31]. Once the sensor hierarchy presented in Figure 7 was determined, data classification was performed using six classifiers. Almost all relevance metrics identified sensors P3_2, P1_2 and P2_2 as the three most relevant. The least significant sensor according to all selection methods was P4_2. The effectiveness of each algorithm was calculated using Equation (1)  
\[\text{Performance}=\frac{\text{TPR}}{\text{TPR}+\text{FPR}}\times 100\% \tag{1}\]  
where TPR means True Positive Rate and FPR stands for False Positive Rate. The effectiveness of each algorithm for a limited number of sensors is shown in Figure 8.  
Figure 8: Classification efficiency as a function of the number of sensors used. Figure 7: Importance of specific sensors. The average gesture recognition performance of each tested algorithm from all conducted experiments is shown in Table 1.  
The highest level of correct classifications was achieved by the \(k\)-NNiRS classifier when working on all 10 sensors. A slightly lower recognition rate was obtained for the Bagged Trees method. The BT method, in contrast to \(k\)-NNiRS, maintains a performance above 94% regardless of the number of sensors and their placement (Figures 8 and 9). To verify how reducing the number of dimensions in the feature vector affects the efficiency of distinguishing individual characters, the confusion matrix was built for the algorithm with the highest mean efficiency. Additionally, we re-run the classification process ignoring data from the first interpha-langeal (IP) and then the second metacarpophalangeal (MCP) row of sensors to check how placement over specific joints impacts the recognition rates. The obtained results allow us to conclude that sensor placement over specific joints does not affect the performance of most classifiers. For the Classification Tree and Naive Bayes Classifier, the placement on the metacarpophalangeal joints achieving a 20% improvement in recognition rate. For the three data classifiers with the highest average performance, the dependence of classification performance on the dimensionality of the feature vector was also tested. In this case, components of the feature vector were sequentially included into the classification process starting with those with the highest importance index as determined by the MRMR method. \begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Average Recognition Rate [\%]** \\ \hline Classification Tree & 76.7 \\ Kernel Naive Bayes Classifier & 74.6 \\ Cubic SVM & 91.5 \\ \(k\)-NN & 93.7 \\ \(k\)-NN in RS & 91.5 \\ Bagged Trees & 93.4 \\ \hline \hline \end{tabular}
\end{table}