# Recognition of Hand Gesture Sequences by Accelerometers and Gyroscopes
## 4 Experimental Results
Therefore, although the proposed neural networks were trained by sequences with \(K=1\), they can be applied to sequences with larger \(K\) values. The high reusability of the proposed models for different \(K\) values is an advantage of the proposed algorithms. Examples of a testing sequences captured by gyroscopes consisting of three (Gesture 11, Gesture 8, and Gesture 10) and four hand gestures (Gesture 3, Gesture 7, Gesture 5, and Gesture 11) are revealed in Figures 15 and 16, respectively. The results of gesture spotting by PairNet are annotated in the bottom of the figures. For the sake of brevity, the waveforms produced by the accelerometers are not included. Small and fast vibrations on some of the waveforms can be found in the figures because of unstable  
Figure 14: Samples of waveforms produced by accelerometers in three axes (gx, gy, and gz) for each gesture. and/or shaking hands. Therefore, gesture spotting may be difficult even by direct visual inspection. Nevertheless, it can be observed from the bottom of Figure 15 that the size of sets \(I_{11}\), \(I_{8}\), and \(I_{10}\) are the largest as compared with other sets. The results are consistent with the ground truth. Furthermore, the sets \(I_{3}\), \(I_{7}\), \(I_{5}\), and \(I_{11}\) have the largest size in Figure 16. Accurate classification is then possible based on the gesture spotting results provided by the PairNet. To evaluate the performance of the proposed feedforward algorithms, we first consider the classification accuracy of each gesture class for the algorithms. Let \(H_{i}\) of an algorithm be the hit rate of gesture class \(i\) for the algorithm in the testing set. In the experiments, the hit rate \(H_{i}\) of an algorithm was equal to the number of gestures in class \(i\), which are correctly classified by the algorithm divided by the total number of gestures in class \(i\) in the testing set. Tables 3 and 4 contain the model summary of the proposed algorithms and their classification accuracy, respectively. For comparison purposes, the 1D CNN [22], LSTM [8], and Bidirectional LSTM (Bi-LSTM) [19] algorithms are also considered. For each algorithm, models from 20 independent training operations were acquired. The hit rates of the 20 models of each given algorithm over the testing set were measured. The hit rates of the best model of each algorithm are reported in Table 4.  
Figure 16: An example of test sequence produced by a gyroscope containing four gestures (Gesture 3, Gesture 7, Gesture 5, and Gesture 11). The bottom of the figure reveals the gesture-spotting results by PairNet. Figure 15: An example of test sequence produced by a gyroscope containing three gestures (Gesture 11, Gesture 8, and Gesture 10). The bottom of the figure reveals the gesture-spotting results by PairNet. We can see from Table 3 that all five feedforward algorithms (i.e., CNN, PairNet, Residual Pairnet, PairNet with inception, and Residual PairNet with inception) have five convolution/inception layers. However, it can be observed from Table 4 that the CNN has a lower classification accuracy when compared to the other four models. In particular, the average hit rates of CNN and PairNet are 90.42% and 92.36%, respectively. The PairNet outperforms the CNN in average hit rate by 1.94%. The CNN is inferior because it is based on convolution operations with a stride size of 1 for all the convolution layers. As a result, it has smaller receptive field for classification. By contrast, the convolution layers of the proposed algorithms have a stride size of 2. They may then have a larger receptive field for attaining better classification accuracy.