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Topic in computer science and language technology

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, it employs mathematical algorithms to interpret gestures. 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.

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, and in contrast, offline gestures are usually processed after the interaction is finished; e. g. a circle is drawn to activate a context menu. Offline gestures: Those gestures that are processed after the user's interaction with the object. An example is a gesture to activate a menu. Online gestures: Direct manipulation gestures. They are used to scale or rotate a tangible object.

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.

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.

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. 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. 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.

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. 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 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. 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. 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.

Public Installations, such as interactive public displays, allow access to information and displays interactive media in public settings such as museums, galleries, and theaters. 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. In order to measure arm fatigue side effect, researchers developed a technique called Consumed Endurance.