A Gesture Recognition Strategy Based on A-Mode Ultrasound for Identifying Known and Unknown Gestures

Human-machine interface(HMI) technology has gradually become a research hotspot with the continuous development of computer technology and Internet of Things (IOT) technology. Hand gesture recognition (HGR) as an important part of HMI technology has been widely concerned. Among the many technical routes of HGR technology, the wearable HGR technology based on A-mode ultrasound shows great application potential due to its advantages such as lightweight device, free from sensors’ constraint etc. However, in the process of processing A-mode ultrasonic signal, the amplitude of the signal at the main position may vary due to muscle fatigue and strength etc. when the same gesture is repeated some time later. In this paper, we design a method to overcome this problem and accomplish HGR in offline state by setting the threshold and use normalization method for the energy feature of ultrasonic signal according to the threshold. Three machine learning algorithms, including support vector machine (SVM), linear discriminant analysis (LDA) and Naive Bayes (NB) were used to verify the feasibility of this method, and the average recognition accuracy in the experiment reached 95.26%. Signal stability is also verified to be improved. In addition, We design a recognition strategy based on NB algorithm in this paper so that the model can identify the unknown gestures (i.e. gestures that have not been trained by the model). Five known gestures and five unknown gestures were set in the experiment, the average recognition accuracy of the known gestures is 91.2%, and the unknown gestures is 81.8%. This method can be used to optimize the user experience of HGR system.