# AnyGesture: Arbitrary One-Handed Gestures for Augmented, Virtual, and Mixed Reality Applications
###### Abstract  
Natural user interfaces based on hand gestures are becoming increasingly popular. The need for expensive hardware left a wide range of interaction possibilities that hand tracking enables largely unexplored. Recently, hand tracking has been built into inexpensive and widely available hardware, allowing more and more people access to this technology. This work provides researchers and users with a simple yet effective way to implement various one-handed gestures to enable deeper exploration of gesture-based interactions and interfaces. To this end, this work provides a framework for design, prototyping, testing, and implementation of one-handed gestures. The proposed framework was implemented with two main goals: First, it should be able to recognize any one-handed gesture. Secondly, the design and implementation of gestures should be as simple as performing the gesture and pressing a button to record it. The contribution of this paper is a simple yet unique way to record and recognize static and dynamic one-handed gestures. A static gesture can be captured with a template matching approach, while dynamic gestures use previously captured spatial information. The presented approach was evaluated in a user study with 33 participants and the implementable gestures received high accuracy and user acceptance.  
g 2022 aca 20  
role in the the recognition of sign language, as used by [PERSON] et al. [16]. New applications based on hand gestures are constantly being developed. Commonly available hardware to detect and track human body parts contributed significantly to the development of gesture-based interactions and interfaces.  
Frameworks from HMD manufacturers usually allow an easy integration of hand gestures for AR and VR applications. These frameworks, however, are often tied to specific hardware and have a limited amount of available gestures. Defining new gestures is not possible or complicated. Additionally, dynamic gestures, such as drawing a sign in the air, concatenation of gestures, or gestures composed of complex hand movements, are not supported.  
Researchers implement various gestures with rather complex methodology and require many training samples in order to implement a reliable gesture. One of the most used approaches is the Hidden Markov Model (HMM), which requires many samples to complete graph optimization, and the process of training is relatively complicated. Many existing frameworks are therefore either not generalizable, complex to use, or tied to specific hardware.  
This paper proposes a solution with a simple yet effective way to record and recognize any one-handed gesture. The gestures that can be implemented with the proposed framework are ready to be used for many different applications. This contribution has the intention of supporting a variety of hardware and should be seen as a complementary solution and not as a replacement of existing frameworks and toolkits. Part of this work was inspired by the $1 recognizer from [PERSON] et al. [17] in the sense that defining and recognizing gestures should be cheap, easy, and flexible to design. In order to evaluate the proposed methodology, a user study was conducted.  
The contributions of this paper are:  
* The possibility to capture arbitrary one-handed static and dynamic gestures via button press, enabling rapid design, prototyping, testing, and implementation of one-handed gestures. This includes dynamic gestures with changing hand shape;
* A simple yet unique way to record and recognize dynamic one-handed gestures;
* A comprehensive user study to evaluate the proposed method.