# AnyGesture: Arbitrary One-Handed Gestures for Augmented, Virtual, and Mixed Reality Applications
## 2 Background and Related Work
### Gesture Design Tools with Similar Objectives  
This section covers the closest gesture-based interface prototyping tools to AnyGesture. These works have similar objectives such as creating gestures without expert knowledge and rapid prototyping of gestures. However, AnyGesture is clearly distinguishable from existing work. A comparison is depicted in Table 1. As an example, [PERSON] et al. introduced the tool MAGIC [36], which allows creating and prototyping gestures with a three-axis accelerometer. Unlike the system presented in this paper, it cannot recognize hand shapes. [PERSON] et al. [37] proposes GestureWiz, a system that is capable of recording and recognizing gestures from video input data. The system allows rapid prototyping for gestures with a consumer-grade webcam. Compared to this system, AnyGesture works with hand skeleton data and is mainly focused on egocentric vision, which makes it more attractive for AR, VR, and MR applications.  
The system Mogeste is proposed by [PERSON] et al. [39], which allows prototyping and testing of gestures realized with inertial sensors on commodity wearable and mobile devices. [PERSON] and [PERSON] [40] propose a system for rapidly prototyping multi-touch gestures. Their system is able to generate gestures that can then be used for multi-touch gesture-based interfaces.  
[PERSON] et al. proposes Kinect Analysis [41] to inspect and annotate motion recordings obtained from depth cameras that could then potentially be reused to create gestures. [PERSON] et al. introduced Gesture Knitter [38], a tool which allows users to combine fine and gross primitives with a visual scripting language. As an example, users are able to combine a static fist gesture (fine primitive) with a forward movement (gross primitive) to create a \"fist-forward\" gesture. The authors of [38] train HMMs to recognize gestures that require training samples as opposed to AnyGesture. Furthermore, Gesture Knitter uses the hand's center of mass to track and detect the movements. This allows only static hand shapes to  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Easy Design of** & **Hand Movement** & **Hand Shape** & **Individual Finger** & **No Training** \\  & **New Gestures** & **Recognition** & **Recognition** & **Movement** & **Data Required** \\ \hline MAGIC [36] & ✓ & ✓ & ✗ & ✗ & ✗ \\ GestureWiz [37] & ✓ & ✓ & ✓ & ✗ & ✗ \\ Gesture Knitter [38] & ✓ & ✓ & ✓ & ✗ & ✗ \\
**AnyGesture** & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison and differentiation with similar systems in existing literature. All mentioned systems have the aim of allowing the rapid design of new gestures. AnyGesture is the only system that does not require multiple training samples and allows gestures that involve individual finger movements. The green check mark indicates availability, the red cross no availability.  
be formed and recognized, while AnyGesture additionally allows movement of individual fingers to form a gesture. This allows AnyGesture to record and recognize a wider range of gestures, including microgestures where only small subtle finger movements are involved.