# Recognition of Hand Gesture Sequences by Accelerometers and Gyroscopes
## 4 Experimental Results
It can be shown in Table 6 that simple gestures such as Gesture 1 and Gesture 3 have slightly lower hit rate because they may be misclassified as a part of other more complicated gestures. Nevertheless, the hit rates of these classes are still above 80%. For complicated gestures such as Gesture 5, Gesture 7, Gesture 10, and Gesture 11, the corresponding hit rates are above 99%. As compared with the basic CNN, the proposed algorithms may have both the advantages of higher classification accuracy, lower storage overhead, and lower computation complexity. In this study, we define the storage overhead and computation complexity of a neural network as the parameter size and the number of multiplications for the inference operations of that network, respectively. Table 7 shows the average hit rate, parameter size, and the number of multiplications of the algorithms considered in Table 3. It can be observed from the figure that all the proposed feedforward models have higher average hit rate, lower weight size, and lower number of multiplications as compared with basic CNN. This is because all the proposed models have a common feature that the intermediate convolution layers are with kernel size \(1\times 2\) and a stride size of 2. By contrast, all the convolution layers of the CNN have kernel size \(1\times 3\) and a stride size of 1. Smaller kernel sizes adopted by the proposed networks are beneficial for lowering the parameter sizes and computation complexities. Furthermore, larger stride sizes could enhance classification accuracy. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **Gest.** & **Gest.** & **Gest.** & **Gest.** & **Gest.** & **Gest.** & **Gest.** & **Gest.** & **Gest.** & **Gest.** \\  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** \\ \hline Gest. 1 & 81.86 & 0.00 & 0.40 & 0.00 & 0.00 & 0.40 & 4.40 & 5.83 & 1.81 & 0.00 & 5.30 \\ Gest. 2 & 0.00 & 96.98 & 0.00 & 0.38 & 0.00 & 0.77 & 0.79 & 0.00 & 0.00 & 0.00 & 1.08 \\ Gest. 3 & 0.00 & 0.62 & 82.66 & 0.91 & 0.00 & 5.31 & 0.00 & 2.19 & 4.60 & 3.71 & 0.00 \\ Gest. 4 & 0.00 & 0.39 & 0.35 & 97.29 & 0.00 & 0.75 & 0.00 & 0.00 & 1.21 & 0.00 & 0.00 \\ Gest. 5 & 0.00 & 0.00 & 0.18 & 0.00 & 99.12 & 0.00 & 0.00 & 0.35 & 0.00 & 0.35 & 0.00 \\ Gest. 6 & 0.00 & 0.00 & 0.29 & 0.00 & 0.00 & 97.93 & 0.00 & 0.00 & 0.89 & 0.89 & 0.00 \\ Gest. 7 & 0.04 & 0.23 & 0.03 & 0.00 & 0.00 & 0.24 & 98.99 & 0.01 & 0.00 & 0.22 & 0.23 \\ Gest. 8 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 2.11 & 0.62 & 96.63 & 0.00 & 0.00 & 0.64 \\ Gest. 9 & 0.00 & 0.35 & 0.00 & 0.00 & 0.00 & 5.15 & 0.00 & 1.93 & 92.57 & 0.00 & 0.00 \\ Gest. 10 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 100.0 & 0.00 \\ Gest. 11 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.46 & 0.00 & 99.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The confusion matrix of the Residual PairNet over the testing dataset. The cell located at row \(i\) and column \(j\) of the matrix represents the percentage in which the gesture in the row \(i\) is classified as the gesture in the column \(j\). The improvement in classification accuracy of the PairNet and its variants over basic CNN is due to the employment of both a stride size of 2 and a kernel size of \(1\times 2\). The improvement would be marginal when only a stride size of 2 is adopted, while the kernel size retains the same as \(1\times 3\). To elaborate this fact, a new CNN with a stride size of 2 is considered, where its kernel sizes are \(1\times 3\) for all the convolution layers. To achieve meaningful comparisons, the new CNN and PairNet have the same number of convolution layers, and the same number of output channels associated with each convolution layer. Furthermore, 20 models of the new CNN algorithms were trained, and the best model only achieved the average hit rate of 90.78%. By contrast, the best average hit rate of PaiNet is 92.36%.