# Hand Trajectory Recognition by Radar with a Finite-State Machine and a Bi-LSTM
## 1 Introduction
Then, the range time map (RTM) and Doppler time map (DTM) of the target are used as inputs of the CNN to classify the radar signal image based on learning the nonlinear characteristics of each digit. The experimental results show that the proposed digit recognition method can recognize air-writing digits from 0 to 9, and the accuracy is greater than 98%. In addition, compared with the standard CNN method, the proposed transfer learning method exhibits superior performance in distinguishing each digit. In [24], [PERSON] et al. proposed a CubeLearn model that directly extracted features from raw radar signals and constructed an end-to-end deep neural network for millimeter-wave FMCW radar gesture recognition. The experimental results show that the proposed CubeLearn model improves classification accuracy and can be used in edge computing devices due to its high computational efficiency. In [25], [PERSON] et al. proposed an optimal detection method for segmenting hand gesture data in real-time recognition systems. Their approach achieved an impressive accuracy of 96.88%, which represents a significant improvement of 11.84% over traditional methods. This enhanced accuracy is critical for the effective segmentation of frames, thereby improving the overall performance of real-time gesture recognition systems. In [26], [PERSON] et al. proposed a hand gesture recognition system for real-time applications using 60 GHz FMCW radar. The system includes both signal processing and machine learning components, with an LSTM encoder designed to learn the temporal characteristics of the RDM sequences. The proposed system successfully distinguishes between 10 gestures collected from 10 participants, achieving a high classification accuracy of 99.10%. It also maintains a high accuracy of 98.48% for gestures from new participants. However, there are still some shortcomings in the existing research. Firstly, due to the fact that there are multiple gesture types that are required to be classified, using a single neural network to recognize all the gestures will result in insufficient recognition accuracy. Secondly, due to the lack of consideration for the complexity of different gesture actions and the massive input of all gesture data for training, the input data are large, the network model is complex, and the recognition efficiency is low. Furthermore, general research only considers collecting all gesture data and compiling them into a dataset for neural network training and classification without considering gesture segmentation issues, which cannot be practically applied in real-time gesture recognition scenarios. Therefore, with the aim of solving the above issues, a radar gesture recognition method based on state machine combined with bidirectional LSTM fusion neural network is proposed in this context. By categorizing gesture actions as either a simple trajectory gesture or a complex trajectory gesture in advance, six simple trajectory gestures are recognized using state machine, and four complex trajectory gestures are recognized using bidirectional LSTM fusion neural network. For simple trajectory gestures, as the hand only moves in a straight line, there is no need to use complex neural networks to recognize it. Instead, a state machine is used to determine the motion status of the gesture at different moments to recognize the current gesture in real time. When gestures cannot be recognized by the state machine, a bidirectional LSTM fusion neural network is used to classify the collected data and recognize complex trajectory gestures. Finally, by combining the classification results of state machines and neural networks, the recognized gesture types are output. Therefore, the main contributions of this article are as follows.