# Hand Trajectory Recognition by Radar with a Finite-State Machine and a Bi-LSTM
## 1 Introduction  
Gesture is an important approach in human-machine interaction. Gestures as the most frequently used body language that can help people exchange information and express emotions in daily communications. Therefore, gestures can be applied to many scenarios in daily life [1], such as virtual reality [2], somatosensory games [3], smart homes [4], and car control [5]. By now, existing gesture recognition methods are implemented based on some hardware such as wearable sensors [6; 7; 8; 9] and visual image sensors [10; 11; 12], which have achieved good results in specific situations. However, these gesture recognition technologies still have some limitations, such as inconvenience in use, susceptibility to environmental interference, and privacy disclosure. With the improvement of the semiconductor manufacturing level and the development of signal processing algorithms, millimeter-wave radar is gradually being applied everywhere [13; 14; 15]. Gesture recognition based on millimeter-wave radar can effectively compensate the shortcomings of the wearable and visual image sensors. Gesture recognition based on radar has the following advantages: (1) There is no need to wear additional equipment. (2) It is not affected by light intensity, dust, haze, etc., and can work normally all day and in all weather conditions. (3) Due to the penetrability of electromagnetic waves,the radar can be integrated into the equipment to increase stability. (4) It is unable to obtain user's personal information, ensuring privacy. At present, some institutions and scholars have studied several relevant methods of gesture recognition based on millimeter-wave radar [16; 17; 18; 19; 20; 21]. In [16], Google systematically studied the solution of radar gesture recognition, and customized the entire set of hardware and algorithm. Finally, relying on its integrated miniaturized radar chip, gesture recognition was implemented efficiently. And it was successfully integrated into the Pixel4 phone, which took the first step towards commercialization. In [17], the Nvidia Company fused millimeter-wave radar, optics, and depth-of-field sensors in order to exploit the complementary advantage of different sensors. And then they used a convolutional neural network (CNN) to classify and recognize the fused data. Finally, the recognition accuracy of 10 kinds of gestures reached 94.1%, and it was successfully applied to the vehicle system. In [18], Infineon proposed a two-dimensional CNN long short-term memory (2D CNN-LSTM) network for gesture recognition. Considering the fact that gesture actions are far from the radar in some applications, this research mainly focused on gesture recognition in long-distance scenarios. Finally, we achieved an accuracy of 94.75% in recognizing five gestures at long distances. To improve the effectiveness and the ability of gesture recognition in complex scenes, some scholars have applied different deep learning methods in gesture recognition and achieved good results. In [22], [PERSON] et al. proposed a variational autoencoder architecture-based deep metric learning, which is optimized using a novel loss function combining a statistical distance triplet loss and center loss. This method can learn the nonlinear characteristics in data better and has less sensitivity to training strategies. Finally, the experimental results show that compared with existing deep metric learning methods, the proposed gesture recognition system using radar spectrograms improves the accuracy of classification recognition and the ability to suppress random gesture actions. In [23], [PERSON] et al. proposed a method for recognizing air-writing digits based on ultra-wideband (UWB) and frequency-modulated continuous wave (FMCW) radar using transfer learning. Firstly, this method processes the radar echo signal to remove clutter signals and converts the radar echo signal into appropriate data formats.