- Human Computer Interaction Using Hand Gestures
- Gesture recognition
- Gesture-Based Communication in Human-Computer Interaction
- International GestureWorkshop, GW’99 Gif-sur-Yvette, France, March 17-19, 1999 Proceedings
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- Gesture-based communication in human-computer interaction pdf
Gesture recognition is a topic in computer science and language technology with the goal of interpreting human gestures via mathematical algorithms.
Gestures can originate from any bodily motion or state but commonly originate from the face or hand. Current [ when? Users can use simple gestures to control or interact with devices without physically touching them. Many approaches have been made using cameras and computer vision algorithms to interpret sign language.
However, the identification and recognition of posture, gait, proxemics , and human behaviors is also the subject of gesture recognition techniques. Using the concept of gesture recognition, it is possible to point a finger at this point will move accordingly. This could make conventional input on devices such and even redundant. The major application areas of gesture recognition in the current [ when?
Gesture recognition can be conducted with techniques from computer vision and image processing. The literature includes ongoing work in the computer vision field on capturing gestures or more general human pose and movements by cameras connected to a computer. Gesture recognition and pen computing: Pen computing reduces the hardware impact of a system and also increases the range of physical world objects usable for control beyond traditional digital objects like keyboards and mice.
Such implementations could enable a new range of hardware that does not require monitors. This idea may lead to the creation of holographic display. The term gesture recognition has been used to refer more narrowly to non-text-input handwriting symbols, such as inking on a graphics tablet , multi-touch gestures, and mouse gesture recognition.
This is computer interaction through the drawing of symbols with a pointing device cursor. In computer interfaces, two types of gestures are distinguished:  We consider online gestures, which can also be regarded as direct manipulations like scaling and rotating. In contrast, offline gestures are usually processed after the interaction is finished; e.
Touchless user interface is an emerging type of technology in relation to gesture control. Touchless user interface TUI is the process of commanding the computer via body motion and gestures without touching a keyboard, mouse, or screen.
Touchless interface in addition to gesture controls are becoming widely popular as they provide the abilities to interact with devices without physically touching them. There are a number of devices utilizing this type of interface such as, smartphones, laptops, games, television, and music equipment.
Companies who are producing or exploring gesture recognition technology include: . White Paper: Explore Intel's user experience research, which shows how touchless multifactor authentication MFA can help healthcare organizations mitigate security risks while improving clinician efficiency, convenience, and patient care.
This touchless MFA solution combines facial recognition and device recognition capabilities for two-factor user authentication. The aim of the project then is to explore the use of touchless interaction within surgical settings, allowing images to be viewed, controlled and manipulated without contact through the use of camera-based gesture recognition technology.
In particular, the project seeks to understand the challenges of these environments for the design and deployment of such systems, as well as articulate the ways in which these technologies may alter surgical practice.
While our primary concerns here are with maintaining conditions of asepsis, the use of these touchless gesture-based technologies offers other potential uses. Tobii Rex: eye-tracking device from Sweden.
Airwriting: technology that allows messages and texts to be written in the air . Myoelectric Armband: allows for communication of bluetooth devices . The ability to track a person's movements and determine what gestures they may be performing can be achieved through various tools.
The kinetic user interfaces KUIs  are an emerging type of user interfaces that allow users to interact with computing devices through the motion of objects and bodies. Examples of KUIs include tangible user interfaces and motion-aware games such as Wii and Microsoft's Kinect ,and other interactive projects.
Human Computer Interaction Using Hand Gestures
Another example of this is mouse gesture trackings , where the motion of the mouse is correlated to a symbol being drawn by a person's hand, as is the Wii Remote or the Myo armband or the mForce Wizard wristband , which can study changes in acceleration over time to represent gestures.
The software also compensates for human tremor and inadvertent movement.
The sensors of these smart light emitting cubes can be used to sense hands and fingers as well as other objects nearby, and can be used to process data. Most applications are in music and sound synthesis,  but can be applied to other fields. Depending on the type of the input data, the approach for interpreting a gesture could be done in different ways. However, most of the techniques rely on key pointers represented in a 3D coordinate system.
Based on the relative motion of these, the gesture can be detected with a high accuracy, depending on the quality of the input and the algorithm's approach. In order to interpret movements of the body, one has to classify them according to common properties and the message the movements may express.
Gesture-Based Communication in Human-Computer Interaction
For example, in sign language each gesture represents a word or phrase. Some literature differentiates 2 different approaches in gesture recognition: a 3D model based and an appearance-based. On the other hand, Appearance-based systems use images or videos for direct interpretation. The 3D model approach can use volumetric or skeletal models, or even a combination of the two. Volumetric approaches have been heavily used in computer animation industry and for computer vision purposes.
The drawback of this method is that it is very computational intensive, and systems for real time analysis are still to be developed. For the moment, a more interesting approach would be to map simple primitive objects to the person's most important body parts for example cylinders for the arms and neck, sphere for the head and analyse the way these interact with each other.
Furthermore, some abstract structures like super-quadrics and generalised cylinders may be even more suitable for approximating the body parts. Instead of using intensive processing of the 3D models and dealing with a lot of parameters, one can just use a simplified version of joint angle parameters along with segment lengths. This is known as a skeletal representation of the body, where a virtual skeleton of the person is computed and parts of the body are mapped to certain segments.
The analysis here is done using the position and orientation of these segments and the relation between each one of them for example the angle between the joints and the relative position or orientation. These models don't use a spatial representation of the body anymore, because they derive the parameters directly from the images or videos using a template database.
Some are based on the deformable 2D templates of the human parts of the body, particularly hands. Deformable templates are sets of points on the outline of an object, used as interpolation nodes for the object's outline approximation.
One of the simplest interpolation function is linear, which performs an average shape from point sets, point variability parameters and external deformators.
These template-based models are mostly used for hand-tracking, but could also be of use for simple gesture classification. A second approach in gesture detecting using appearance-based models uses image sequences as gesture templates. Parameters for this method are either the images themselves, or certain features derived from these.
Most of the time, only one monoscopic or two stereoscopic views are used. There are many challenges associated with the accuracy and usefulness of gesture recognition software.
For image-based gesture recognition there are limitations on the equipment used and image noise. Images or video may not be under consistent lighting, or in the same location. Items in the background or distinct features of the users may make recognition more difficult.
International GestureWorkshop, GW’99 Gif-sur-Yvette, France, March 17-19, 1999 Proceedings
The variety of implementations for image-based gesture recognition may also cause issue for viability of the technology to general usage. For example, an algorithm calibrated for one camera may not work for a different camera. The amount of background noise also causes tracking and recognition difficulties, especially when occlusions partial and full occur. Furthermore, the distance from the camera, and the camera's resolution and quality, also cause variations in recognition accuracy.
In order to capture human gestures by visual sensors, robust computer vision methods are also required, for example for hand tracking and hand posture recognition          or for capturing movements of the head, facial expressions or gaze direction.
One significant challenge to the adoption of gesture interfaces on consumer mobile devices such as smartphones and smartwatches stems from the social acceptability implications of gestural input. While gestures can facilitate fast and accurate input on many novel form-factor computers, their adoption and usefulness is often limited by social factors rather than technical ones.
To this end, designers of gesture input methods may seek to balance both technical considerations and user willingness to perform gestures in different social contexts. Gesture interfaces on mobile and small form-factor devices are often supported by the presence of motion sensors such as inertial measurement units IMUs.
On these devices, gesture sensing relies on users performing movement-based gestures capable of being recognized by these motion sensors. This can potentially make capturing signal from subtle or low-motion gestures challenging, as they may become difficult to distinguish from natural movements or noise.
Through a survey and study of gesture usability, researchers found that gestures that incorporate subtle movement, which appear similar to existing technology, look or feel similar to every actions, and which are enjoyable were more likely to be accepted by users, while gestures that look strange, are uncomfortable to perform, interferes with communication, or involves uncommon movement caused users more likely to reject their usage.
Wearable computers typically differ from traditional mobile devices in that their usage and interaction location takes place on the user's body. In these contexts, gesture interfaces may become preferred over traditional input methods, as their small size renders touch-screens or keyboards less appealing.
Nevertheless, they share many of the same social acceptability obstacles as mobile devices when it comes to gestural interaction.
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However, the possibility of wearable computers to be hidden from sight or integrated in other everyday objects, such as clothing, allow gesture input to mimic common clothing interactions, such as adjusting a shirt collar or rubbing one's front pant pocket. A study exploring third-party attitudes towards wearable device interaction conducted across the United States and South Korea found differences in the perception of wearable computing use of males and females, in part due to different areas of the body considered as socially sensitive.
Public Installations , such as interactive public displays, allow access to information and displaying interactive media in public settings such as museums, galleries, and theaters. This effect contributed to the decline of touch-screen input despite initial popularity in the s. In order to measure arm fatigue and the gorilla arm side effect, researchers developed a technique called Consumed Endurance. From Wikipedia, the free encyclopedia.
Gesture-based communication in human-computer interaction pdf
This article's tone or style may not reflect the encyclopedic tone used on Wikipedia. See Wikipedia's guide to writing better articles for suggestions. November Learn how and when to remove this template message. Retrieved Cipolla and A. Archived from the original on Microsoft Research. Pallotta; P. Bruegger; B. Hirsbrunner February IGI Publishing. Benford; H. Schnadelbach; B.