## 2 Theoretical Background
### Implementation Methods for Gesture-Based User Interfaces  
Gesture-based UIs can be classified into touch-based and touchless methods. As a touch-based method, the user directly wears and interacts with devices capable of detecting the user's motion. Accurate user gesture information can be obtained using relatively expensive equipment. A representative device for implementing touch-based gesture interfaces is the Data Glove [24], which was invented in the 1980s and can provide real-time tracking of hand position and orientation for gesture recognition. Currently, several commercial data gloves exist for motion capture and animation experts. However, the user is burdened with wearing the equipment, and errors may accumulate if the equipment is used for a long time. Another disadvantage is that the equipment is typically expensive, and the calibration process is complicated. The touchless gesture interface recognizes a user's movement using a single camera, multiple cameras, or other sensors. Typically, initialization, tracking, user posture prediction, and gesture recognition are performed. In the initialization process, the camera is calibrated by determining its internal and external parameters. The tracking process continuously tracks feature points from a sequence of captured images depicting the movement of the user. The user's posture is predicted using the tracked feature points, and the movement is recognized. This method allows for more natural movement for the user compared to touch-based methods, but errors may occur in finding and tracking the feature points of the user's movement. To overcome this problem, a touchless gesture interface can utilize a marker. Two types of markers are used: an active marker with an LED and infrared light and a passive marker using color. Although this method increases the recognition rate, the difference from those of the touch-based gesture interfaces is negligible. Therefore, markerless and touchless gesture tracking using only a camera can benefit the most from touchless gesture interfaces. \begin{table}
\begin{tabular}{c c} \hline \hline
**Type of NUI** & **Method** \\ \hline Gesture interface & Recognizes the user’s motion as a command. \\ \hline Touch interface & Recognizes the user’s touch as a command. \\ \hline Voice interface & Recognizes the user’s voice as a command. \\ \hline Brain–computer interface & Reads the user’s EEG signal (brain waves) and translates them into system commands. \\ \hline Eye interface & Recognizes the user’s eye movements, gaze direction, and blinks as commands. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Types of natural user interfaces (NUIs) [21]. Leap Motion and Kinect are representative markerless and touchless gesture interface devices. Kinect recognizes the entire body of the user and is used not only for entertainment but also for sports, physical activity, and rehabilitation [25]. Because Kinect tracks the entire body, recognizing delicate gestures, such as hand gestures, is difficult. In contrast, Leap Motion is a high-sensitivity hand gesture recognition device that can recognize up to 0.01 m of hand motion at 290 frames-per-second using an infrared camera [26]. It detects independent movements of fingers and thumbs and recognizes the three-dimensional position and direction information of the fingertips, as well as the direction information of the palm. In addition, it is relatively inexpensive, and the device size is small; therefore, it is easy to mount on wearable devices. For this reason, Leap Motion may have the potential to be used as an interface in various platforms and applications. However, the Leap Motion controller has limited operating conditions and field of view; when hands are overlapped or interlocked, the recognition rate of joint information tends to decrease. Therefore, an interface design suitable for the operating environment is required. However, Leap Motion can act as a substitute for more expensive sensors in the industry and on the market [25].