Hand gesture recognition based on Bayesian sensing hidden Markov models and Bhattacharyya divergence

Chen, Sih-Huei; Hernawan, Ari; Lee, Yuan-Shan; Wang, Jia-Ching

doi:10.1109/icip.2017.8296940

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…Several works on gesture detection were published in the 1990s. The techniques were based on several classifiers for hand gesture recognition (HGR), including the k-nearest neighbors’ algorithm, support vector machines (SVMs), neural networks (NNs), and finite-state machines (FSNs), in addition to hidden Markov models and neural networks for calibration [ 51 , 52 , 53 , 54 , 55 , 56 ]. Table 3 and Table 4 show the detection techniques for static and dynamic gestures, respectively.…”

Section: Methodsmentioning

confidence: 99%

Static and Dynamic Hand Gestures: A Review of Techniques of Virtual Reality Manipulation

Herbert,

Pérez-Granados,

Ruiz

et al. 2024

Sensors

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This review explores the historical and current significance of gestures as a universal form of communication with a focus on hand gestures in virtual reality applications. It highlights the evolution of gesture detection systems from the 1990s, which used computer algorithms to find patterns in static images, to the present day where advances in sensor technology, artificial intelligence, and computing power have enabled real-time gesture recognition. The paper emphasizes the role of hand gestures in virtual reality (VR), a field that creates immersive digital experiences through the Ma blending of 3D modeling, sound effects, and sensing technology. This review presents state-of-the-art hardware and software techniques used in hand gesture detection, primarily for VR applications. It discusses the challenges in hand gesture detection, classifies gestures as static and dynamic, and grades their detection difficulty. This paper also reviews the haptic devices used in VR and their advantages and challenges. It provides an overview of the process used in hand gesture acquisition, from inputs and pre-processing to pose detection, for both static and dynamic gestures.

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