Deep Learning-based Kick Motion Recognition in Millimeter Wave Band Radar System

초록

In this paper, 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 provides 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 employing 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 two-dimensional fast Fourier transform and multiple antenna elements. Additionally, 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 treats 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 one-dimensional convolutional neural network achieved more than 97% in accuracy, F1 score, recall and precision. Furthermore, when compared with the models soly based on velocity information, overall performances decreased to around 66%, proving the effectiveness of the MIMO FMCW radar-based kick motion recognition. IEEE

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