The concept of kick sensors is aiding users to open or close vehicle doors applying a simple kick gesture using the foot. These sensors have usually been implemented using ultrasound, capacitive sensing, computer vision, and \(\mathbf {24\textbf{GHz}}\) Continuous Wave (CW) radar. This paper discusses the algorithm development and implementation of a kick sensor on a \(\mathbf {60\textbf{GHz}}\) frequency modulated CW radar platform using deep learning. The goal is to develop a robust yet cost-effective solution for real-time kick gesture recognition. This has been achieved using one transmitting and one receiving antenna, an efficient data compression approach, and a convolutional neural network with a low memory requirement that is capable of achieving \(\mathbf {97\%}\) accuracy on test data. The final prototype can detect kicks and send control signals to open or close a vehicle’s tailgate at an accuracy level of \(\mathbf {88\%}\) . Future improvements are discussed as well.