  This paper comprehensively reviews hand gesture datasets based on Ultraleap's
leap motion controller, a popular device for capturing and tracking hand
gestures in real-time. The aim is to offer researchers and practitioners a
valuable resource for developing and evaluating gesture recognition algorithms.
The review compares various datasets found in the literature, considering
factors such as target domain, dataset size, gesture diversity, subject
numbers, and data modality. The strengths and limitations of each dataset are
discussed, along with the applications and research areas in which they have
been utilized. An experimental evaluation of the leap motion controller 2
device is conducted to assess its capabilities in generating gesture data for
various applications, specifically focusing on touchless interactive systems
and virtual reality. This review serves as a roadmap for researchers, aiding
them in selecting appropriate datasets for their specific gesture recognition
tasks and advancing the field of hand gesture recognition using leap motion
controller technology.
