# Reducing the Number of Sensors in the Data Glove for Recognition of Static Hand Gestures
## 5 Discussion  
An interesting feature of the PSL is that it has diacritics that are dynamic versions of corresponding letters. This feature is important for further research involving recognition of dynamic signs. The initial phase of the study focused on testing the overall performance of the data glove. A significant challenge was the recognition of gestures with similar finger arrangements. As shown in Figure 10, the differences in two similar gestures captured by the presented glove are visible to the naked eye. The finger placements for both gestures are very similar to each other and differ only in the flexion of one joint. Importantly, reducing the dimensionality of the feature vector according to the designated sensor importance did not result in a significant decrease in the classification performance of the recognized characters (Figure 11). Figure 11: Graphs of gesture classification performance by algorithms with the highest average performance against the number of sensors. Figure 10: The average signal values of each sensor for the gestures corresponding to letters “O” and “S”. Reducing the dimensionality to five features resulted in a 97.5% effectiveness for \(k\)-NNiRS and BT algorithms. Although the efficiency of the \(k\)-NNiRS algorithm turned out to be the highest of all classifier for 10 sensors, it decreased most rapidly as the dimensionality of the feature vector was reduced. This is due to the main assumption of the algorithm, which includes self-reduction method of the feature vector to a randomly selected few of its components. This explains the apparent decrease in classification efficiency of the \(k\)-NNiRS algorithm, as seen in Figure 11, while the BT and \(k\)-NN algorithms achieve efficiency of 85%. The \(k\)-NNiRS algorithm in this situation uses only a one-dimensional feature vector. Table 3 compares the work presented in Related Work with the results presented in this paper. It should be noted that the solutions for recognizing dynamic gestures and static gestures cannot be directly compared due to the different methods used. The main contribution of study is the method of reducing the number of sensors needed to recognize static gestures based on Polish Sign Language to 3 out of 10 sensors. As can be seen in Table 3, no device presented in the previous works used a device with such a small number of sensors. An efficiency of 95.1% was achieved by BT and \(k\)-NN classifiers for the three-component feature vector (Figure 8). A more than ten-fold increase in FPR was observed for several hand positions including those corresponding to letters \"W\", \"O\", \"U\" and \"R\". The gestures corresponding to letters \"W\" and \"U\" were most often confused with the gesture corresponding to letter \"R\". This is most likely because all these gestures require the index and middle finger to be fully straightened, while overlapping these fingers while performing the gesture corresponding to letter \"R\" requires detection of a movement that is not performed in the plane of movement of the sensor. The decrease in TPR for the letter \"O\" can be explained in a similar way. It relies mainly on the index finger and thumb, with middle finger straight. The exclusion from the classification process of the sensor located above the interpha-langeal joints of the index finger, which flexes the most during this gesture, may result in a false prediction of the gesture. Nevertheless, the performance obtained can be considered satisfactory and promising, as it remains above or close to 90% for most gestures. This is a result worth noting because \(k\)-NN and BT are classifiers with short training time and low computational complexity.