Deep-Learning-Based Kick Motion Recognition in Millimeter Waveband Radar System

In this article, we propose a method for recognizing kick motions using a multiple-input multiple-output (MIMO) frequency-modulated continuous wave (FMCW) radar system combined with deep learning techniques. Smart trunk opener (STO) systems that provide users with hands-free trunk operation have been gaining attention. To address the limitations of prevalent STO systems, which rely on capacitive or ultrasonic sensors, we propose using a 60-GHz MIMO FMCW radar system. Our design of a 60-GHz MIMO FMCW radar system can detect kick motions and estimate their range, velocity, and angle, using a signal processing chain that includes a 2-D fast Fourier transform and multiple antenna elements. In addition, using the acquired information on the range, velocity, and angle of the kick motion, we propose a deep-learning-based model for recognizing specific kick motions to operate the STO system. This model is designed to take sequences of range, velocity, and angle as its input, unlike the conventional motion recognition methods that treat radar data as an image. We analyze the performances of various deep-learning-based models using a dataset of various kick motions obtained through real-life measurements. Results showed that the 1-D convolutional neural network (CNN) achieved more than 97% in accuracy, $F1$ score, recall, and precision. Furthermore, when compared with the models solely based on velocity information, overall performances decreased to around 66%, proving the effectiveness of the MIMO FMCW radar-based kick motion recognition.