An improved denoising method for eye blink detection using automotive millimeter wave radar

Abstract

With the development of radar technology, the automotive millimeter wave radar is widely applied in the fields including internet of vehicles, Artificial Intelligence (AI)-based autonomous driving, health monitoring, etc. Eye blink, as one of the most common human activities, can effectively reflect the person’s consciousness and fatigue. The contacted eye blink detection often leads to uncomfortable experience and the camera-based eye blink detection has privacy issues. As an alternative, the non-contacted eye blink detection based on automotive millimeter wave radar resolves the aforementioned issues and has been received much attention. This paper proposes an eye blink detection method using the frequency modulated continuous wave radar. Firstly, the position of the person’s head is estimated by carrying out fast Fourier transform on the intermediate frequency signal, and the signals of the range bins at the head are extracted. Then, the complete ensemble empirical mode decomposition with adaptive noise algorithm is applied to decompose the eye signals into a series of intrinsic mode functions (IMFs), and the singular value decomposition is adopted to constrain the selection and reconstruction of the useful IMFs related to the eye blink signal. Finally, the short-time Fourier transformation and cell average constant false alarm rate are applied to detect the eye blink behavior. Experiments are carried out to validate the effectiveness of the proposed eye blink detection method.