# Single-Handed Gesture Recognition with RGB Camera for Drone Motion Control
## 2 Related Works  
Human-machine interaction has evolved significantly over the years, with applications ranging from entertainment to critical assistance in industrial and personal domains. Effective communication between humans and machines is essential for successful interaction. Traditionally, human-machine interaction relies on learning to operate conventional controllers, which often requires familiarity with various buttons and complex functions, posing challenges for beginners. Recent advancements have introduced natural user interfaces (NUIs) as a transformative approach, offering more intuitive methods for interaction by enabling users to interact with devices similarly to the ways in which they interact with the physical world, using voice commands, hand gestures, and body movements [25]. For instance, video game components like the Wiimote and Kinect allow users to experience gaming in an immersive and natural manner [26]. Recent years have seen a significant rise in research exploring gesture-based interactions across a range of human-machine systems. In particular, gesture control extends to broader human-robot interaction (HRI) contexts. For instance, [PERSON] et al. [5] developed a multi-sensor guided hand gesture recognition system for teleoperated robots, highlighting the effectiveness of intuitive hand gestures in robotic control scenarios. Similarly, [PERSON] et al. [6] used multimodal data fusion to enhance hand gesture recognition for human-robot interaction, emphasizing the value of robust and flexible gesture-based communication in diverse robotics applications. These studies illustrate the applicability of hand gestures in providing an intuitive interface for the control of various robotic systems, showcasing the versatility and utility of gesture-based interaction for effective control. Human-drone interaction, in particular, has emerged as a promising field with applications ranging from surveillance to delivery services and entertainment [27]. Among various modalities, hand gestures stand out as being highly intuitive and natural, making them particularly suitable for drone control. Designing an optimized hand gesture set is therefore crucial to enhance the interaction between humans and drones. The drone's flight involves complex motions represented by a combination of four independent factors: roll, yaw, pitch, and throttle. Each movement axis can express three behaviors: increase, decrease, and neutral. In order to enable the drone to navigate through complex motions within unfamiliar and cluttered environments, it is essential to consider well-aligned hand gestures of high accuracy that can represent at least 81 (i.e., \(3^{4}\)) motion combinations. [PERSON] et al. [8] suggested a single-hand gesture set corresponding to the direction of drone flight. They developed a set of gestures to control the direction of the drone flight; however, each gesture can only represent one motion at a time, potentially limiting the dynamics of the drone flight. [PERSON] et al. [22] also introduced intuitive hand gestures for a drone control vocabulary; however, their gesture set encompassed a restricted range, potentially constraining its capacity to address complex flight scenarios. [PERSON] et al. [23] conducted a comprehensive study to examine the comfort and intuitiveness of hand gestures associated with flight concepts across a broad spectrum of scenarios. They designed two sets of gestures, categorized as palm- and finger-based, and assessed two distinct modes of gesture control across a varied user demographic, encompassing individuals of both genders, as well as first responders and members of the general population. Referring to this, we design a set of gestures, as detailed in Section 3.2. After designing a set of hand gestures, consideration must be given to the methods of acquiring and classifying the gesture data. The sources for data acquisition in HGR algorithms can be categorized into two approaches: image-based and non-image-based methods [8].