# A Method of Ultrasonic Finger Gesture Recognition Based on the Micro-Doppler Effect
## 1 Introduction  
Touchscreen control is now used in most mobile devices, such as mobile phones and tablets. When a person uses it with a wet hand or gloved hand, the touch will not work well. With the rapid development of mobile devices, such as small-sized smart watches, it is also very inconvenient to control devices on a small screen. As a part of human communication, gestures can be used to express a wide variety of emotions and thoughts. Gestures are usually the second most natural method of interaction between humans and the environment, as well as among humans [1]. Gestures are convenient and have a vast interaction space and super high flexibility, providing excellent interactive experience. Therefore, gestures in human-computer interactions have gained greater attention in recent years [2]. A variety of gesture recognition methods have been proposed [3; 4; 5; 6; 7; 8; 9; 10]. These methods can usually be divided into four categories: wearable sensor-based, optical vision-based, electromagnetic sensor-based and ultrasound-based. _Wearable sensor-based:_ Many gesture recognition methods based on wearable sensors have been reported [3; 4; 11; 12]. [PERSON] et al. [3] explored a data glove as the input device and recognized five kinds of predefined hand gestures. [PERSON] and [PERSON] [12] presented an accelerometer-based pen-type sensing device and a user-independent hand gesture recognition algorithm that achieved almost perfect user-independent recognition accuracies. All of these methods require the user to wear additional sensors on the hand or arm. In contrast, ultrasonic-based gesture recognition methods directly sense the movements of the hand without any wearable device. _Optical vision-based:_ Optical vision sensors, including color cameras, depth cameras and infrared cameras, are the most widely used in gesture recognition because of the successful commercial productsMicrosoft Kinect and Leap Motion, which can capture human body activities by cameras [5; 6; 13], [PERSON] et al. [6] developed a natural gesture user interface that could track and recognize hand gesture based on depth data collected by a Kinect sensor. [PERSON] et al. [14] presented a system that is capable of learning gestures by using the data from the Leap Motion device and the Hidden Markov classification algorithm. Although optical vision-based methods have good recognition performance, they are susceptible to illumination conditions and ambient infrared radiation [15]. In addition, these methods also have to entail high computational costs. _Electromagnetic sensor-based:_ Electromagnetic sensors have been widely used for human activity classification [7; 8; 16; 17] and gesture recognition [18; 19; 20; 21; 22; 23; 24; 25; 26]. Most of the proposed methods for gesture recognition based on electromagnetic sensors can only recognized hand gestures of big movements, but some methods for classifying finger gestures have also been reported, such as [PERSON]'s method [23] and project Soli [22; 27]. WiFiinger [23] presented a fine-grained finger gesture recognition system by using a single commodity WiFi device and achieved a recognition accuracy of over 93%. However, the system is impractical in the outdoors considering the availability of commercial wireless Access Points to connect the WiFi device. Soli [27] is a new gesture sensing technology designed by Google and designated to be an interaction sensor that uses radar for motion tracking of the human hand. The key part of Soli's design is a dedicated radar chip, incorporating the entire sensor and antenna array into an ultra-compact 8 mm \(\times\) 10 mm package. In contrast, the proposed method in this paper just requires a pair of common commercial sensors, a simpler hardware setting and low-cost computing resources. _Ultrasound-based:_ Ultrasound-based methods of gesture recognition can be generally classified into three categories via different schemes. First, the Doppler effect has been used to sense gesture for a long time [9; 10; 28; 29; 30; 31; 32].