Human machine interaction (HMI) is an interactive way of information exchange between human and machine. By collecting the information that can be conveyed by the person to express the intention, and then transforming and processing the information, the machine can work according to the intention of the person. However, the traditional HMI including mouse, keyboard etc. usually requires a fixed operating space, which limits people's actions and cannot directly reflect people's intentions. It requires people to learn systematically how to operate skillfully, which indirectly affects work efficiency. Hand gesture, as one of the important ways for human to convey information and express intuitive intention, has the advantages of high degree of differentiation, strong flexibility and high efficiency of information transmission, which makes hand gesture recognition (HGR) as one of the research hotspots in the field of HMI. In order to enable readers to systematically and quickly understand the research status of HGR and grasp the basic problems and development direction of HGR, this article takes the sensing method used by HGR technology as the entry point, and makes a detailed elaboration and systematic summary by referring to a large number of research achievements in recent years.

The YOLOv4 neural network is employed for underwater target recognition. To improve the accuracy and speed of recognition, the structure of YOLOv4 is modified by replacing the upsampling module with a deconvolution module and by incorporating depthwise separable convolution into the network. Moreover, the training set used in the YOLO network is preprocessed by using a modified mosaic augmentation, in which the gray world algorithm is used to derive two images when performing mosaic augmentation. The recognition results and the comparison with the other target detectors demonstrate the effectiveness of the proposed YOLOv4 structure and the method of data preprocessing. According to both subjective and objective evaluation, the proposed target recognition strategy can effectively improve the accuracy and speed of underwater target recognition and reduce the requirement of hardware performance as well.

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.