The ability of computers to recognise hand gestures visually is essential for progress in human-computer interaction. Gesture recognition has applications ranging from sign language to medical assistance to virtual reality. However, gesture recognition is extremely challenging not only because of its diverse contexts, multiple interpretations, and spatio-temporal variations but also because of the complex non-rigid properties of the hand. This study surveys major constraints on vision-based gesture recognition occurring in detection and pre-processing, representation and feature extraction, and recognition. Current challenges are explored in detail.

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Review of constraints on vision-based gesture recognition for human–computer interaction

Hand gestures recognition (HGR) is one of the main areas of research for the engineers, scientists and bioinformatics. HGR is the natural way of Human Machine interaction and today many researchers in the academia and industry are working on different application to make interactions more easy, natural and convenient without wearing any extra device. HGR can be applied from games control to vision enabled robot control, from virtual reality to smart home systems. In this paper we are discussing work done in the area of hand gesture recognition where focus is on the intelligent approaches including soft computing based methods like artificial neural network, fuzzy logic, genetic algorithms etc. The methods in the preprocessing of image for segmentation and hand image construction also taken into study. Most researchers used fingertips for hand detection in appearance based modeling. Finally the comparison of results given by different researchers is also presented.

Intelligent Approaches to interact with Machines using Hand Gesture Recognition in Natural way: A Survey

A method or system for selecting or pruning applicable verbal commands associated with speech recognition based on a user's motions detected from a depth camera. Depending on the depth of the user's hand or arm, the context of the verbal command is determined and verbal commands corresponding to the determined context are selected. Speech recognition is then performed on an audio signal using the selected verbal commands. By using an appropriate set of verbal commands, the accuracy of the speech recognition is increased.

Depth based context identification

Hand gestures recognition is the natural way of Human Machine interaction and today many researchers in the academia and industry are interested in this direction. It enables human being to interact with machine very easily and conveniently without wearing any extra device. It can be applied from sign language recognition to robot control and from virtual reality to intelligent home systems. In this paper we are discussing work done in the area of hand gesture recognition where focus is on the soft computing based methods like artificial neural network, fuzzy logic, genetic algorithms, etc. We also described hand detection methods in the preprocessed image for detecting the hand image. Most researchers used fingertips for hand detection in appearance based modeling. Finally we are comparing results given by different researchers after their implementation.

A Survey on Hand Gesture Recognition in Context of Soft Computing

While considerable technology has been integrated into the operating room, until recently, the actual performance of surgery has seen relatively few changes, relying mainly on hand-eye coordination. This paper outlines the development and composition as well as the requirements and reasoning that lead to the human-machine interface on neuroArm, a telerobotic surgical system. A critical component of the system was the workstation, where information was provided to and received from the operator. The surgeon controls the robotic system using two force-feedback hand controllers based on visual information from a stereoscopic viewing device and two liquid crystal displays. Two touch screens allow the user to monitor and control the settings of the robot and to view and manipulate 3-D MR images. Audio feedback from the surgical site and the operating room staff is also provided by a wireless communication system. The workstation components were chosen not only to recreate the sight, sound, and touch of surgery but also to facilitate the integration of surgeons with advanced imaging and robotic technologies.

Human–Machine Interface for Robotic Surgery and Stereotaxy

Gesture based HMI offers the possibility of substantial safety benefits, but there is conflicting evidence on user acceptance, most researchers concluding that users prefer gesture controls. An alternative system is being developed that an automotive gesture recognition system using electric field sensors can achieve the same safety benefits as vision-based systems, at lower cost, without the same technical difficulties.

The search for a safer driver interface: a review of gesture recognition human machine interface

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The search for a safer driver interface: a review of gesture recognition human machine interface