# Sensor-Based Hand Gesture Detection and Recognition by Key Intervals
## 4 Experimental Results  
This section presents some experimental results of the proposed algorithm. In the experiments, all the gesture sequences for training and testing were acquired by a smartphone equipped with an accelerometer and a gyroscope. The sensors were capable of measuring acceleration and angular velocity in three orthogonal axes, respectively. Therefore, the dimension of each sample \(s_{t}\) was \(N=6\). The size of windows \(S_{t}\) was \(W=50\). The sampling rate was 50 samples/s. For the inference operations, the stride size was \(\Delta=1\). The smart phones adopted for the experiments were a Samsung Galaxy S8 and an HTC ONE M9. A Java-based app was built on the smartphones for gesture capturing and delivery. As shown in Figure 5, there were five foreground gesture classes (i.e., \(K=5\)) for the detection and classification. Samples of foreground gestures were recorded as training data for the training of the proposed network model. Let \(M_{\text{m}}\) be the number of the gestures in the training set. In the experiments, training sets with \(M_{\text{m}}=180,320,450,590,750\) gestures were considered. In this way, the impact on the performance of the proposed algorithm for different sizes of training sets could be evaluated. Samples of gestures for each foreground class in the training set are shown in Figure 6. The training operations are implemented by [PERSON] [33]. The testing set for performance evaluation was different from the training sets. In addition, there were gestures in the test sequences which did not belong to the foreground  
Figure 5: The five foreground gesture classes considered in both training and testing sets. Figure 6: Sensory sequences of foreground gestures (i.e., Gesture 1, Gesture 2, Gesture 3, Gesture 4, Gesture 5 defined in Figure 5) in the training set. The boundary of each gesture is marked in the corresponding sequences. Left column: sensory sequence in three orthogonal axes produced by accelerometer; right column: sensory sequence in three orthogonal axes produced by gyroscope, (**a**) Gesture 1, (**b**) Gesture 2, (**c**) Gesture 3, (**d**) Gesture 4, and (**e**) Gesture 5.  
gesture classes defined in Figure 5. These gestures were termed background gestures in this study. Figure 7 shows the background gesture classes considered in the experiments. These background classes contained only simple gestures which could be parts of the foreground gesture. In this way, the effectiveness of the proposed algorithm for ignoring background gestures and detecting foreground gestures could be evaluated. Let \(M_{\textsc{tr}}\) be the number of gestures in the testing set. In the experiments, there were 1503 sequences in the testing set. Each sequence may contain two or more hand gestures. The total number of gestures in the testing set was \(M_{\textsc{tr}}=3283\). Among these testing gestures, 1617 gestures were foreground gestures, and 1666 gestures were background gestures. The initial orientation of the smartphone for data acquisition of both training and testing sequences was the portrait orientation. All our experimental results were evaluated on the same testing set. The network model adopted by the experiments is shown in Figure 2. There were eight convolution layers and three dense layers in the model. Table 1 shows the number of weights in each layer, the number of weights for the backbone, and the branches in the model. We can see from Table 1 that the backbone had the largest number of weights compared with the branches in the model. This was because the backbone contained four convolution layers for effective feature extraction. A fewer number of convolution layers were needed in the branches. The total number of weights in the model was only 282,142, which is the sum of the number of weights for the backbone and branches. The separate evaluations of gesture detection and classification are first presented.