# Eyelid Dynamics Characterization with 120 GHz mmW Radar
## 6 Discussion  
It is important to explore the impact of various circumstances on signal processing and examine the feasibility of extracting data for the successful detection of eyelid blinks in different environments. In the previous measurements, the sweep bandwidth was set to 3 GHz and was later reduced to 1 GHz by applying a tapered time domain window to the beating tone of the radar. As a result, the resolution cell in the direction of propagation, defined by the bandwidth \(\Delta f\), increased from 5 cm to 15 cm. With accurate alignment and cancellation of the face background, no substantial loss of information occurred due to a reduction in bandwidth. In recent years, the majority of the research on eyelid detection has relied on optical and electromagnetic methods. Camera-based algorithms, however, often face challenges, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Start** & **End** & **Duration** \\ \hline Duration & \(t_{\text{obs}}=10.40\) & \(t_{\text{obs}}=10.70\) & 300 ms \\ Closing & \(t_{\text{obs}}=10.40\) & \(t_{\text{obs}}=10.47\) & 70 ms \\ Closed eyes & \(t_{\text{obs}}=10.47\) & \(t_{\text{obs}}=10.51\) & 40 ms \\ Opening & \(t_{\text{obs}}=10.51\) & \(t_{\text{obs}}=10.70\) & 190 ms \\ \hline \(\overline{T}=2.99\) s & \(\sigma_{T}=238\) ms & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Extracted time characteristics of the eye blink at \(t_{\text{obs}}=10.5\) s in Figures 10 and 11. Dynamics of different blinks, like closing speed, for example, can be generated from such data. The average blink period and the standard deviation in the blink period for the whole acquisition are also shown. Figure 11: The blink signal at approximately 10.5 s in Figure 10. (**a**) Phase evolution (blue line) and magnitude (red line). (**b**) Phase of the blink process (blue line) with the sampling data of the eye aperture (red line) during this process. including factors such as facial features, posture, lighting conditions, the angle of view of the lens, and distance from the camera. For instance, the advanced algorithm proposed in [21] is highly dependent on a face detection system in capturing the subject's face. Additionally, the algorithm exhibits sensitivity to the distance of the subject, and its performance is significantly hindered when the subject's eyes' natural colors and contours are altered with makeup. The method proposed in [22] for monitoring eye movements using electromagnetic sensing technology requires the inclusion of magnetic materials into the eyelashes, which may pose potential risks and inconvenience. In contrast, the above issues can be effectively mitigated using the radar technique proposed, which offer robustness to illumination conditions without requiring any kind of contact or eye manipulation. Meanwhile, it was tested whether wearing glasses would affect the measurement of eyelid movement. This is an important consideration if a patient that uses glasses needs to be monitored. Wearing glasses did not have a significant impact on the results. It is worth noting that certain limitations may affect the use of radar technology to measure eyelid dynamics signals. In our experiments, the measurements were conducted in a controlled indoor environment that was clean, static, and free from complex interference. Consequently, external noise and environmental factors did not significantly impact the measurement accuracy or stability. However, in more complex scenarios, such as monitoring drivers, the robustness of the algorithm may be compromised. The small size of the eyelid as a target, combined with the potential movements of the subject, introduces challenges in terms of the radar's precision in target alignment. This difficulty in maintaining accurate radar pointing could reduce the reliability in complex cases.