# Recognition of Hand Gesture Sequences by Accelerometers and Gyroscopes
## 4 Experimental Results
\begin{table}
\begin{tabular}{l l} \hline \hline
**Model** & **Summary** \\ \hline PairNet & 5 Conv. layers, 1 Avg. polling layer, 1 FC layer \\  & Stride Size 1 and kernel size 1 \(\times\) 3 for Conv. layer 1 \\  & Stride Size 2 and kernel size 1 \(\times\) 2 for Conv. layers 2–5 \\  & Softmax output \\ \hline Residual & 5 Conv. layers, 1 Avg. polling layer, 1 FC layer \\ PairNet & Stride Size 1 and kernel size 1 \(\times\) 3 for Conv. layer 1 \\  & Stride Size 2 and kernel size 1 \(\times\) 2 for Conv. layers 2–5 \\  & 2 Short-cut connections, Softmax output \\ \hline PairNet with & 4 Conv. layers, 1 Inception layer \\ Inception & 1 Avg. polling layer, 1 FC layer \\  & Stride Size 1 and kernel size 1 \(\times\) 3 for Conv. layer 1 \\  & Stride Size 2 and kernel size 1 \(\times\) 2 for Conv. layers 2–4 \\  & Softmax output \\ \hline Residual & 4 Conv. layers, 1 Inception layer \\ PairNet with & 1 Avg. polling layer, 1 FC layer \\ Inception & Stride Size 1 and kernel size 1 \(\times\) 3 for Conv. layer 1 \\  & Stride Size 2 and kernel size 1 \(\times\) 2 for Conv. layers 2–4 \\  & 1 Short-cut connection, Softmax output \\ \hline CNN & 5 Conv. Layers, 2 Max. polling layer, 1 FC layer \\  & Stride Size 1 and kernel size 1 \(\times\) 3 for Conv. layers 1–5 \\  & Softmax output \\ \hline LSTM & Hidden states and mem. cells for single direction \\  & Hidden states and mem. cells dimension = 32 \\  & 1 FC layer with Softmax output \\ \hline Bi-LSTM & Hidden states and mem. cells for dual directions \\  & Hidden states and mem. cells dimension = 32 \\  & 1 FC layer with Softmax output \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The model summary of various algorithms. In addition to larger stride sizes, it is revealed from Table 4 that the incorporation of short-cut connections and/or inception modules are also beneficial for the improvement of classification accuracy. That is, Residual Pairnet, PairNet with inception, and Residual PairNet with inception have superior hit rates over those of PairNet. In fact, the experimental results show that the average hit rates of PairNet, Residual Pairnet, PairNet with inception, and Residual PairNet with inception are 92.36%, 95.30%, 94.00%, and 94.10%, respectively. The improvement in average hit rate over the PairNet by Residual Pairnet, PairNet with inception, and Residual PairNet with inception are then 2.94%, 1.64%, and 1.74%, respectively. The improvements are due to the facts that the employment of short-cut connections may be able to provide more reliable references for training. Furthermore, the inception modules are able to effectively capture both slow and fast hand movements in a single layer. Note that the hit rates in Table 4 are the best hit rates for the corresponding algorithms. To further assess Residual Pairnet, PairNet with inception, and Residual PairNet with inception algorithms, the statistical evaluations over the hit rates for each algorithm are included in Table 5. The evaluation includes the measurements of highest, mean, and lowest average hit rates over the 20 models associated with each algorithm. It can be observed from Table 5 that these statistical measurements for PairNet are inferior to those for Residual Pairnet, PairNet with inception, and Residual PairNet with inception algorithms. These results confirm the superiority of the proposed algorithms over their baseline PairNet counterpart. An additional advantage of the proposed algorithms is that they outperform recurrent algorithms such as LSTM and Bi-LSTM, as shown in Table 4. The model summary of the LSTM and Bi-LSTM in our experiments can also be found in Table 3. The LSTM has inferior hit rates because of the gradient vanishing problem. The performance can be improved by bidirectional training and inference.