# Recognition of Hand Gesture Sequences by Accelerometers and Gyroscopes
## 4 Experimental Results
The new CNN does not perform well because the large kernel size of \(1\times 3\) introduces a large number of parameters (i.e., 400,369) so that overfitting may be likely. To provide better generalization for the training, smaller kernel size with short-cut connections and/or inception modules would be more effective. Among the proposed feedforward algorithms, it can be observed from Table 7 that the PairNet with Inception has lowest parameter size and computation complexity. This is because the dimension reduction is carried out by front kernel in each path of the inception module shown in Figure 8. The total number of kernels can then be effectively reduced. This in turn may lower the parameter size and computation complexity. Because inception modules may be beneficial for reducing the model complexity, the Residual PairNet with inception also has lower complexity as compared with its Residual PairNet counterpart without inception, as shown in Table 7.  
It can also be observed from Table 7 that the LSTM algorithm has the lowest computational complexity. Although there are four pairs of matrices used in the cell and hidden state calculations in the LSTM, the dimension of cells and hidden states is only 32. This is beneficial for maintaining small number of multiplications for the algorithm. However, the inference operations of the LSTM should be carried out in a recurrent fashion. Therefore, the reduction in computation time may not be significant as compared with the feedforward algorithms. To elaborate this fact, we have measured the computation time of LSTM, PairNet and CNN on the Raspberry Pi 3 platform, where the computation time of a network is the average CPU time for the inference of a single gesture from the testing set of that network. Based on the experiments, the computation time of LSTM, PairNet, and CNN are 102, 162, and 410 ms, respectively. The computation time of PairNet is only slightly longer than that of LSTM. This is because the inference operations of the feedforward networks can be computed in parallel. Therefore, the real-time inference may still be possible even the system is deployed in the embedded systems with Raspberry Pi 3 platform. Finally, we evaluate the performance of the proposed algorithms when only one of the sensors is adopted. Table 8 shows the results of the experiments for the PairNet. We can see from Table 8 that the hit rates of most gesture classes are degraded without the employment of both sensors. These results show that the employment of both gyroscope and accelerometer is beneficial for hand-gesture recognition. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **PairNet** & **CNN** & **LSTM** & **Bi-** & **Res.** & **PairNet** & **Res. PairNet** \\  & **[22]** & **[8]** & **LSTM [19]** & **PairNet** & **w. Incep.** & **w. Incep.** \\ \hline Average & & & & & & & \\ Hit Rate & & & & & & & \\ \hline Weights & & & & & & & \\ Size & & & & & & & \\ \hline Number of & & & & & & & \\ Multiplications & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The comparisons on average hit rates, number of weights, and computation complexity of various neural networks. The computation complexity of a neural network is the total number of multiplications required for the inference operations of the neural network.