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Gesture recognition is an area of research and development in computer science and language technology concerned with the recognition and interpretation of human gestures. A subdiscipline of computer vision,[ citation needed ] it employs mathematical algorithms to interpret gestures. [1]
Gesture recognition offers a path for computers to begin to better understand and interpret human body language, previously not possible through text or unenhanced graphical (GUI) user interfaces.
Gestures can originate from any bodily motion or state, but commonly originate from the face or hand. One area of the field is emotion recognition derived from facial expressions and hand gestures. Users can make 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. [2]
Gesture recognition has application in such areas as:[ when? ]
Gesture recognition can be conducted with techniques from computer vision and image processing. [5]
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. [6] [7] [8] [9]
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. [10] [11] [12] Pen computing expands digital gesture recognition beyond traditional input devices such as keyboards and mice, and reduces the hardware impact of a system.[ how? ]
In computer interfaces, two types of gestures are distinguished: [13] We consider online gestures, which can also be regarded as direct manipulations like scaling and rotating, and in contrast, offline gestures are usually processed after the interaction is finished; e. g. a circle is drawn to activate a context menu.
A touchless user interface (TUI) is an emerging type of technology wherein a device is controlled via body motion and gestures without touching a keyboard, mouse, or screen. [14]
There are several devices utilizing this type of interface such as smartphones, laptops, games, TVs, and music equipment.
One type of touchless interface uses the Bluetooth connectivity of a smartphone to activate a company's visitor management system. This eliminates having to touch an interface, for convenience or to avoid a potential source of contamination as during the COVID-19 pandemic. [15]
The ability to track a person's movements and determine what gestures they may be performing can be achieved through various tools. 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.[ citation needed ] Examples of KUIs include tangible user interfaces and motion-aware games such as Wii and Microsoft's Kinect, and other interactive projects. [16]
Although there is a large amount of research done in image/video-based gesture recognition, there is some variation in the tools and environments used between implementations.
Depending on the type of 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 high accuracy, depending on the quality of the input and the algorithm's approach. [30]
In order to interpret movements of the body, one has to classify them according to common properties and the message the movements may express. 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. [31] The foremost method makes use of 3D information on key elements of the body parts in order to obtain several important parameters, like palm position or joint angles. Approaches derived from it such as the volumetric models have proven to be very intensive in terms of computational power and require further technological developments in order to be implemented for real-time analysis. Alternately, appearance-based systems use images or videos for direct interpretation. Such models are easier to process, but usually lack the generality required for human-computer interaction.
The 3D model approach can use volumetric or skeletal models or even a combination of the two. Volumetric approaches have been heavily used in the computer animation industry and for computer vision purposes. The models are generally created from complicated 3D surfaces, like NURBS or polygon meshes.
The drawback of this method is that it is very computationally 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 analyze the way these interact with each other. Furthermore, some abstract structures like super-quadrics and generalized cylinders maybe 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)
Advantages of using skeletal models:
Appearance-based models no longer use a spatial representation of the body, instead deriving their 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 the 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 functions is linear, which performs an average shape from point sets, point variability parameters, and external deformation. These template-based models are mostly used for hand-tracking, but could also be used for simple gesture classification.
The second approach in gesture detection 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.
Electromyography (EMG) concerns the study of electrical signals produced by muscles in the body. Through classification of data received from the arm muscles, it is possible to classify the action and thus input the gesture to external software. [1] Consumer EMG devices allow for non-invasive approaches such as an arm or leg band and connect via Bluetooth. Due to this, EMG has an advantage over visual methods since the user does not need to face a camera to give input, enabling more freedom of movement.
There are many challenges associated with the accuracy and usefulness of gesture recognition and software designed to implement it. 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.
The variety of implementations for image-based gesture recognition may also cause issues with the viability of the technology for 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 [32] [33] [34] [35] [36] [37] [38] [39] [40] or for capturing movements of the head, facial expressions or gaze direction.
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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 are 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. [41] In addition, different device hardware and sensing mechanisms support different kinds of recognizable gestures.
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 signals 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 action, and are enjoyable were more likely to be accepted by users, while gestures that look strange, are uncomfortable to perform, interfere with communication, or involve uncommon movement caused users more likely to reject their usage. [41] The social acceptability of mobile device gestures relies heavily on the naturalness of the gesture and social context.
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. However, the possibility of wearable computers being hidden from sight or integrated into 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. [42] [43] A major consideration for wearable computer interaction is the location for device placement and interaction. 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 socially sensitive. [43] Another study investigating the social acceptability of on-body projected interfaces found similar results, with both studies labelling areas around the waist, groin, and upper body (for women) to be least acceptable while areas around the forearm and wrist to be most acceptable. [44]
Public Installations, such as interactive public displays, allow access to information and displays interactive media in public settings such as museums, galleries, and theaters. [45] While touch screens are a frequent form of input for public displays, gesture interfaces provide additional benefits such as improved hygiene, interaction from a distance, and improved discoverability, and may favor performative interaction. [42] An important consideration for gestural interaction with public displays is the high probability or expectation of a spectator audience. [45]
Arm fatigue was a side-effect of vertically oriented touch-screen or light-pen use. In periods of prolonged use, users' arms began to feel fatigued and/or discomfort. This effect contributed to the decline of touch-screen input despite its initial popularity in the 1980s. [46] [47]
In order to measure arm fatigue side effect, researchers developed a technique called Consumed Endurance. [48] [49]
In computing, a pointing device gesture or mouse gesture is a way of combining pointing device or finger movements and clicks that the software recognizes as a specific computer event and responds to accordingly. They can be useful for people who have difficulties typing on a keyboard. For example, in a web browser, a user can navigate to the previously viewed page by pressing the right pointing device button, moving the pointing device briefly to the left, then releasing the button.
A handheld projector is an image projector in a handheld device. It was developed as a computer display device for compact portable devices such as mobile phones, personal digital assistants, and digital cameras, which have sufficient storage capacity to handle presentation materials but are too small to accommodate a display screen that an audience can see easily. Handheld projectors involve miniaturized hardware, and software that can project digital images onto a nearby viewing surface.
A tangible user interface (TUI) is a user interface in which a person interacts with digital information through the physical environment. The initial name was Graspable User Interface, which is no longer used. The purpose of TUI development is to empower collaboration, learning, and design by giving physical forms to digital information, thus taking advantage of the human ability to grasp and manipulate physical objects and materials.
Multimodal interaction provides the user with multiple modes of interacting with a system. A multimodal interface provides several distinct tools for input and output of data.
A wired glove is an input device for human–computer interaction worn like a glove.
In computer security, shoulder surfing is a type of social engineering technique used to obtain information such as personal identification numbers (PINs), passwords and other confidential data by looking over the victim's shoulder. Unauthorized users watch the keystrokes inputted on a device or listen to sensitive information being spoken, which is also known as eavesdropping.
In computing, multi-touch is technology that enables a surface to recognize the presence of more than one point of contact with the surface at the same time. The origins of multitouch began at CERN, MIT, University of Toronto, Carnegie Mellon University and Bell Labs in the 1970s. CERN started using multi-touch screens as early as 1976 for the controls of the Super Proton Synchrotron. Capacitive multi-touch displays were popularized by Apple's iPhone in 2007. Multi-touch may be used to implement additional functionality, such as pinch to zoom or to activate certain subroutines attached to predefined gestures using gesture recognition.
In computing, post-WIMP comprises work on user interfaces, mostly graphical user interfaces, which attempt to go beyond the paradigm of windows, icons, menus and a pointing device, i.e. WIMP interfaces.
Activity recognition aims to recognize the actions and goals of one or more agents from a series of observations on the agents' actions and the environmental conditions. Since the 1980s, this research field has captured the attention of several computer science communities due to its strength in providing personalized support for many different applications and its connection to many different fields of study such as medicine, human-computer interaction, or sociology.
In computer vision and computer graphics, 3D reconstruction is the process of capturing the shape and appearance of real objects. This process can be accomplished either by active or passive methods. If the model is allowed to change its shape in time, this is referred to as non-rigid or spatio-temporal reconstruction.
An interaction technique, user interface technique or input technique is a combination of hardware and software elements that provides a way for computer users to accomplish a single task. For example, one can go back to the previously visited page on a Web browser by either clicking a button, pressing a key, performing a mouse gesture or uttering a speech command. It is a widely used term in human-computer interaction. In particular, the term "new interaction technique" is frequently used to introduce a novel user interface design idea.
In human–computer interaction, an organic user interface (OUI) is defined as a user interface with a non-flat display. After Engelbart and Sutherland's graphical user interface (GUI), which was based on the cathode ray tube (CRT), and Kay and Weiser's ubiquitous computing, which is based on the flat panel liquid-crystal display (LCD), OUI represents one possible third wave of display interaction paradigms, pertaining to multi-shaped and flexible displays. In an OUI, the display surface is always the focus of interaction, and may actively or passively change shape upon analog inputs. These inputs are provided through direct physical gestures, rather than through indirect point-and-click control. Note that the term "Organic" in OUI was derived from organic architecture, referring to the adoption of natural form to design a better fit with human ecology. The term also alludes to the use of organic electronics for this purpose.
In computing, 3D interaction is a form of human-machine interaction where users are able to move and perform interaction in 3D space. Both human and machine process information where the physical position of elements in the 3D space is relevant.
In computing, a natural user interface (NUI) or natural interface is a user interface that is effectively invisible, and remains invisible as the user continuously learns increasingly complex interactions. The word "natural" is used because most computer interfaces use artificial control devices whose operation has to be learned. Examples include voice assistants, such as Alexa and Siri, touch and multitouch interactions on today's mobile phones and tablets, but also touch interfaces invisibly integrated into the textiles furnitures.
Human–computer interaction (HCI) is research in the design and the use of computer technology, which focuses on the interfaces between people (users) and computers. HCI researchers observe the ways humans interact with computers and design technologies that allow humans to interact with computers in novel ways. A device that allows interaction between human being and a computer is known as a "Human-computer Interface (HCI)".
In the field of gesture recognition and image processing, finger tracking is a high-resolution technique developed in 1969 that is employed to know the consecutive position of the fingers of the user and hence represent objects in 3D. In addition to that, the finger tracking technique is used as a tool of the computer, acting as an external device in our computer, similar to a keyboard and a mouse.
Omek Interactive was a venture-backed technology company developing advanced motion-sensing software for human-computer interaction. Omek was co-founded in 2007 by Janine Kutliroff and Gershom Kutliroff.
Daniel Wigdor is a Canadian computer scientist, entrepreneur, investor, expert witness and author. He is the associate chair of Industrial Relations as well as a professor in the Department of Computer Science at the University of Toronto.
Shumin Zhai is a Chinese-born American Canadian Human–computer interaction (HCI) research scientist and inventor. He is known for his research specifically on input devices and interaction methods, swipe-gesture-based touchscreen keyboards, eye-tracking interfaces, and models of human performance in human-computer interaction. His studies have contributed to both foundational models and understandings of HCI and practical user interface designs and flagship products. He previously worked at IBM where he invented the ShapeWriter text entry method for smartphones, which is a predecessor to the modern Swype keyboard. Dr. Zhai's publications have won the ACM UIST Lasting Impact Award and the IEEE Computer Society Best Paper Award, among others, and he is most known for his research specifically on input devices and interaction methods, swipe-gesture-based touchscreen keyboards, eye-tracking interfaces, and models of human performance in human-computer interaction. Dr. Zhai is currently a Principal Scientist at Google where he leads and directs research, design, and development of human-device input methods and haptics systems.
Joseph J. LaViola Jr. is an American computer scientist, author, consultant, and academic. He holds the Charles N. Millican Professorship in Computer Science and leads the Interactive Computing Experiences Research Cluster at the University of Central Florida (UCF). He also serves as a Consultant at JJL Interface Consultants as well as co-founder of Fluidity Software.
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