# Hand Trajectory Recognition by Radar with a Finite-State Machine and a Bi-LSTM
## 5 Discussion  
The experimental results show that good recognition results can be achieved by dividing gestures into simple trajectory gestures and complex trajectory gestures and classifying them separately. Given the distinct motion characteristics inherent in various types of gestures, customizing recognition methods tailored to each gesture type enables the effective utilization of unique information across different gestures, thereby enhancing both recognition efficiency and accuracy. Therefore, a joint recognition method are proposed to distinguish the two types of gestures. Use the finite-state machine to recognize the simple trajectory gestures and for complex trajectory gestures, a bidirectional LSTM fusion neural network is proposed for recognition. For simple trajectory gestures, due to the fact that the gesture motion trajectory is essentially a straight linear motion, the difference between various gestures is only in the direction of motion. Therefore, the key to recognize gestures through a finite-state machine is to determine the linearity and direction of the gesture motion trajectory. Firstly, determine whether the linearity of the gesture trajectory meets the linear state. Then, determine the direction of the gesture motion trajectory. By combining the results of both judgments, identify the corresponding gesture and achieve gesture recognition. This method uses a direct and simple judgment method to determine the type of gesture, resulting in higher recognition accuracy compared to traditional neural networks. Moreover, due to its smaller computational complexity, the recognition efficiency is also high. In addition, using finite-state machines can help us to make judgments on continuous gesture motion and output a real-time judgment of gesture status even if multiple well-defined gestures are made continuously over a period of time. However, traditional gesture recognition methods require waiting for the current action to be completed before recognition. The proposed method solves the problem of not being able to recognize continuously changing gestures. For complex trajectory gestures, due to the complexity of the gesture motion trajectory, it is not possible to determine the gesture motion status by finite-state machines. There are similar motion parts across different gestures. For example, both the check and cross gestures begin with a motion to the right and downward, which can confuse the finite-state machine, so neural networks are used for recognition. A bidirectional LSTM fusion neural network is proposed for four gestures. Due to the ability of convolutional layers to process input image features, this neural network uses convolutional layers to extract image features from input data. Due to the ability of a bidirectional LSTM structure to effectively capture both pre- and post-correlations in the gesture motion process, it can thoroughly incorporate the sequence features preceding and following the current time point. Therefore, the bidirectional LSTM is used to extract the temporal features during the gesture motion process. The proposed neural network utilizes both features for recognition, fully utilizing the information collected by radar. Therefore, the experimental results show that compared to the three-channel CNN, it has better recognition and classification results. During the training process of the neural network, the initial learning rate parameter of the three-channel CNN and the bidirectional LSTM fusion neural network are adjusted independently. Through experiments, the optimal learning rate is selected to train the neural network. The experimental results show that when the learning rate is set too low, the convergence speed of training is too slow, the training time to achieve the best recognition is too long, and the training efficiency is too low. When the learning rate is set too high, the accuracy curve and loss curve during the training process oscillate and cannot converge to the optimal recognition.