## 4 Experimentation and Analysis of Results
### Experimental Results and Analysis
#### 4.3.2 Robustness Experiments
Faster gestures, however, introduce pronounced signal changes with additional noise and interference, complicating recognition. Slow gestures produce smoother signal transitions, but their subtlety may also reduce recognizability. The Enlarge and Narrow gestures involve more intricate multi-finger coordination compared to simple linear motions. This complexity leads to lower accuracy, especially at extreme speeds where signal variability and uncertainty further increase recognition difficulty. (3) Effect of positional diversity  
The experiments investigated the attenuation and multipath reflection phenomena of electromagnetic signals as the gesture's position relative to the device varied. These variations cause differences in the received signals' amplitude and phase, leading to discrepancies in gesture features related to distance and angle. The impact of these feature variations on gesture recognition accuracy requires further validation. We tested 28 different positions, covering distances (D1 + D2) ranging from 1 m to 4 m and angles (\(\theta\)) of \(0^{\circ}\), \(30^{\circ}\), \(60^{\circ}\), and \(90^{\circ}\). At each position, 10 datasets were collected for each gesture in both office and conference room environments, yielding a total of 560 experimental samples. Using this data, we evaluate the effectiveness of gesture recognition at varying distances and angles to analyze how feature differences affect recognition accuracy. The experimental setup is illustrated in Figure 12, and the results are shown in Table 8. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gestures** & **Standard (4 s)** & **Fast (6 s)** & **Slow (2 s)** \\ \hline Enlarge & 93.69\% & 88.57\% & 91.36\% \\ Narrow & 93.56\% & 87.72\% & 92.54\% \\ Up & 94.31\% & 89.78\% & 92.98\% \\ Down & 94.65\% & 91.75\% & 93.13\% \\ Left & 93.63\% & 89.63\% & 93.37\% \\ Right & 93.88\% & 90.53\% & 93.46\% \\
**Average** & **94.12\%** & **89.66\%** & **92.80\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Accuracy of speed on gesture recognition (%). Figure 12: Graphical representation of device deployment. Based on the data in Table 8, it is evident that recognition accuracy varies significantly with changes in object position and angle, with overall recognition efficiency ranging from 74.36% to 94.25%. Specifically, recognition accuracy peaks at 92.17% when the object is positioned at a 0\({}^{\circ}\) angle and a distance of 1 m. In contrast, when the object is placed at a 90\({}^{\circ}\) angle and the distance increases to 4 m, recognition accuracy drops to a minimum of 78.75%. Further examination indicates that, under a fixed angular condition, recognition accuracy tends to decline progressively as the distance increases. Likewise, when distance remains constant, variations in the angle also lead to a slight decrease in accuracy, though the extent of this reduction is relatively limited. It is worth emphasizing that although recognition accuracy tends to decline as the distance increases, in practical applications such as smart home interaction or virtual reality systems, the user typically operates within a moderate range from the device. Therefore, the observed reduction in accuracy has a limited impact on the overall user experience. In the experimental design, when the angle \(\theta\) between the person and the transceiver device remains constant, the geometry of the signal path stays stable, with the only change being an increase in the total distance (D1 + D2). This alteration results in an elongated signal propagation path, increasing the probability of signal attenuation and interference. On the contrary, assuming that D1 + D2 remains constant, the accuracy decreases slightly as the \(\theta\) angle gradually increases from 0\({}^{\circ}\) to 90\({}^{\circ}\). This occurs because, at \(\theta\) = 0\({}^{\circ}\), the signal travels directly along a straight line, making the path the simplest, and the multipath effect has less influence, resulting in the highest recognition accuracy.