Dynamic Hand Gesture Recognition for Numeral Handwritten via A-Mode Ultrasound

In recent years, due to the defects of weak sEMG signal, insensitive to fine finger movement and serious impression by noise, researchers consider the need to use A-mode ultrasound (AUS) for gesture decoding. However, the current A-mode ultrasonic gesture recognition algorithm is still relatively basic, which can recognize the recognition function of discrete gestures. However, due to the lack of time information, A-mode ultrasound still lacks an algorithm to recognize the dynamic gesture process. Therefore, we design and experiment a deep learning algorithm model applied to AUS signal, which is a deep learning framework based on LSTM. Due to the principle of LSTM, the model sets a certain number of frames as the whole action process, and constructs the connection of each frame in the whole process, so the time correlation (time characteristic) of AUS signal is constructed. Then, the features from AUS signal are sent to the complete full connection layer to output the classification results. And because AUS signal lacks data set of dynamic gestures, we designed and tested handwritten digits 0–9 as an example of dynamic gestures. Experimental results show that this algorithm can realize the dynamic gesture classification of AUS signal and solve the defect of AUS signal lacking time information. In addition, compared with the experimental action of traditional methods, it gives the practical significance of dynamic gesture in life, which is closer to life.