Abstract
Real-time sensing and modeling of the human body, especially the hands, is an important research endeavor for various applicative purposes such as in natural human computer interactions. Hand pose estimation is a big academic and technical challenge due to the complex structure and dexterous movement of human hands. Boosted by advancements from both hardware and artificial intelligence, various prototypes of data gloves and computer-vision-based methods have been proposed for accurate and rapid hand pose estimation in recent years. However, existing reviews either focused on data gloves or on vision methods or were even based on a particular type of camera, such as the depth camera. The purpose of this survey is to conduct a comprehensive and timely review of recent research advances in sensor-based hand pose estimation, including wearable and vision-based solutions. Hand kinematic models are firstly discussed. An in-depth review is conducted on data gloves and vision-based sensor systems with corresponding modeling methods. Particularly, this review also discusses deep-learning-based methods, which are very promising in hand pose estimation. Moreover, the advantages and drawbacks of the current hand gesture estimation methods, the applicative scope, and related challenges are also discussed.
Highlights
With the rapid growth of computer science and related fields, the way that humans interact with computers has evolved towards a more natural and ubiquitous form
Hand gesture recognition is a pattern recognition problem that maps the hand’s appearance and/or motion related features to a gesture vocabulary set, whereas hand pose estimation can be considered as a regression problem that aims to recover the full kinematic structure of hands in 3D space
The goal of this paper is to provide a timely overview of the progress in the field of hand pose estimation, including devices and methods proposed in the last few years
Summary
Weiya Chen 1 , Chenchen Yu 1,2 , Chenyu Tu 1,2 , Zehua Lyu 1 , Jing Tang 2 , Shiqi Ou 1, *, Yan Fu 2,3, *. Received: 12 December 2019; Accepted: 10 January 2020; Published: 16 February 2020
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