In the field of gesture recognition and , 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.
The finger tracking system is focused on user-data interaction, where the user interacts with virtual data, by handling through the fingers the volumetric of a 3D object that we want to represent.
This system was born based on the human-computer interaction problem. The objective is to allow the
communication between them and the use of gestures and hand movements to be more intuitive,
Finger tracking systems have been created. These systems track in real time the position in 3D and 2D of
the orientation of the fingers of each marker and use the intuitive hand movements and gestures to interact.
There are many options for the implementation of finger tracking, principally those used with or without an interface.
This system mostly uses inertial and optical motion capture systems.
Inertial motion capture systems are able to capture finger motion by reading the rotation of each finger segment in 3D space. Applying these rotations to kinematic chain, the whole human hand can be tracked in real time, without occlusion and wireless.
Hand inertial motion capture systems, like for example Synertial mocap gloves, use tiny IMU based sensors, located on each finger segment. Precise capture requires at least 16 sensors to be used. There are also mocap glove models with less sensors (13 / 7 sensors) for which the rest of the finger segments is interpolated (proximal segments) or extrapolated (distal segments). The sensors are typically inserted into textile glove which makes the use of the sensors more comfortable.
Inertial sensors can capture movement in all 3 directions, which means finger and thumb flexion, extension and abduction can be detected.
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Gesture recognition is a topic in computer science and language technology with the goal of interpreting human gestures via mathematical algorithms. It is a subdiscipline of computer vision. Gestures can originate from any bodily motion or state, but commonly originate from the face or hand. Focuses in the field include emotion recognition from face and hand gesture recognition since they are all expressions. Users can make simple gestures to control or interact with devices without physically touching them.
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