# Recognition of Hand Gesture Sequences by Accelerometers and Gyroscopes
## 4 Experimental Results  
This section presents some experimental results for the proposed gesture recognition algorithms and systems. We implemented a gesture-based remote control system for home appliances, shown in Figure 11. The evaluation is then based on the system. There are eleven gesture classes (i.e., \(Q=11\)), where each gesture class is defined in Figure 12. The gesture sequences to be classified are listed in Table 2. They have lengths of two, three, or four (i.e., \(K=2,3\), or 4) dependent on the types of home appliances, actions, or PIN. To facilitate the evaluation, a JAVA-based APP was built on the smartphones for gesture capturing and delivery. We adopted a Samsung Galaxy S8 and HTC ONE M9 for the experiments. The training of feedforward neural networks was carried out offline by [PERSON] [34] with backend TensorFlow. The server for the training was a personal computer with an Intel I7 CPU and NVIDIA GTX 1070 GPU. Raspberry Pi 3 computers are adopted as the embedded systems attached to the home appliances. A python-based inference system is deployed in each embedded system. It is built from the neural network model acquired from [PERSON] after training operations are completed. The inference system is capable of receiving the sensory data from smart phones for real-time dynamic gesture recognition. All the gesture sequences for training and testing were captured by accelerometers and gyroscopes associated with the smart phones. The sensors were capable of measuring acceleration and angular velocity in three orthogonal axes, respectively. Therefore, the dimension of each sample \(x_{t}\) was \(M=6\). Figures 13 and 14 show the samples of waveforms produced by gyroscopes and accelerometers of the \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Gesture Sequences** & **Actions** & **Gesture Sequences** & **Actions** \\ \hline TV & Gest. 5+1 & Volume Up & Gest. 5+2 & Volume Down \\  & Gest. 5+3 & Prev. Chan. & Gest. 5+4 & Next Chan. \\  & Gest. 5+6 & Power On/Off & Gest. 5+7 & Record On/Off \\ PIN for TV & Gest. 11+\(i+j,i\
eq j\
eq 11\) & Authentication & & \\ \hline Air & Gest. 8+1 & Temp. Up & Gest. 8+2 & Temp Down \\ Cond. & Gest. 8+3 & Air Vol. Up & Gest. 8+4 & Air Vol. Down \\ (AC) & Gest. 8+6 & Power On/Off & Gest. 8+7 & Func. Sel. \\ PIN for AC & Gest. 3+\(i+j+k,i\
eq j\
eq k\
eq 3\) & Authentication & & \\ \hline Music & Gest. 9+1 & Volume Up & Gest. 9+2 & Volume Down \\ Player & Gest. 9+3 & Prev. Song & Gest. 9+4 & Next Song \\ (MP) & Gest. 9+6 & Power On/Off & Gest. 9+7 & Source Sel. \\ PIN for MP & Gest. 11+\(i+j,i\
eq j\
eq 11\) & Authentication & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Examples of the gesture sequences and their actions for various home appliances. Each sequence of actions contains two gestures. Each sequence has a personal identification number (PIN) for home appliances’ authentication contains three or four gestures. The definition of gestures are shown in Figure 12.  
Figure 12: The eleven gesture classes considered in the smart home system in this study. In the experiments, each training gesture sequence represents only a single gesture (i.e., \(K=1\)). There were 100 training sequences for each gesture class. Because the number of gesture classes is 11 (i.e., \(Q=11\)), the training set of the experiments consisted of 1100 training gesture sequences. They were acquired from two participants. The sequences were collected on different days for capturing different variations in gesturing, such as variations in lengths of gestures and/or amplitudes of samples. The testing set was different from the training set. It contained 3404 gestures from six participants. For some test sequences, vibrations due to shaking or unstable hands were included for testing the robustness of the proposed algorithm. The initial orientation of smart phones for data acquisition of both training and testing sequences was in the portrait orientation. Each test sequence may contain two, three, or four hand gestures (i.e., \(K=2\), 3, or 4).