# Single-Handed Gesture Recognition with RGB Camera for Drone Motion Control
## 1 Introduction  
Hand gestures are a common form of nonverbal communication in everyday life interactions. They are relatively more natural, private, and intuitive than other forms, such as facial expressions [1], gaze [2], and silent speech [3]. This makes them a valuable tool for interaction with people and machines. Hand gesture recognition (HGR) is the process of extracting information from hand gestures and converting it into a machine-readable format. This can be performed for a variety of applications, such as sign language recognition [4], human-robot interaction [5, 6], virtual environments [7], drone flight control [8], and so on. Moreover, there have also been attempts to use hand gestures to engage with unmanned systems during rescue operations, fostering effective human-machine collaboration in specific environments [9]. HGR can be divided into two main categories: wearable glove-based and camera vision-based approaches [10]. The first HGR for human-computer interaction (HCI) started with the invention of the data glove sensor [11]. Data gloves use different sensor types to capture hand motion and positions. The sensors detect signals of a physical response, such as the bending of the fingers and the movement of the hands, and then use this information to determine the coordinates of the location of the palm and fingers [12]. Since then, various sensors exploiting different physical principles have been used to measure the angle of finger bending and hand location to capture hand motion and positions, such as curvature sensors [13], angular displacement sensors [14], flex sensors [15], and accelerometer sensors [16]. While these methods yield accurate results due to the direct measurement of movements, the inconvenience of wearing them and the high costs of the sensors are problems that reduce their accessibility and usability. In contrast, vision-based HGR has gained popularity due to its ease of use and advances in open-source software. Ithas been applied in various domains, such as assisting visually impaired individuals [17], personal computers [18], robot control [19], clinical operations [20], and so on. Vision-based HGR utilizes different visual sensors, including RGB, time-of-flight (TOF), infrared (IR), thermal, or night vision, to track hand movement. This approach offers advantages such as cost-effectiveness and improved user comfort compared to wearable glove-based methods. Recently, efforts have been made to combine both approaches, known as multimodal hand gesture recognition (HGR), to enhance the performance [21]. In this paper, we propose a simple and efficient single-handed hand gesture recognition (HGR) system for human-machine interaction, primarily designed for human-drone interaction but adaptable to various environments, including mobile and edge devices. Previous works [8; 22; 23; 24] have introduced their own hand gesture vocabularies and HGR models. However, these vocabularies often have a limited number of commands, restricting the drone flight dynamics, and rely on external devices or expensive vision sensors. Our research focuses on a vision-based HGR approach. The objective is to create a user-friendly hand gesture set for the control of dynamic drone movements and to develop a real-time, accurate, lightweight, and efficient vision-based HGR algorithm suitable for mobile computing environments. We begin by analyzing the four fundamental elements of drone movement, which serve as the basis for the design of the new hand gesture set. Subsequently, we construct a simplified and efficient vision-based HGR with an RGB camera sensor. Our contributions to this research field can be summarized in two ways. Firstly, we design a new hand gesture set specifically tailored for human-drone interaction. This gesture set aims to enhance the user convenience while aligning with the movement characteristics of drones. By incorporating the four fundamental movement elements of drones, our gesture set enables the representation of complex flight tasks.