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MGC3140 Arkusz danych(PDF) 11 Page - Microchip Technology

Numer części MGC3140
Szczegółowy opis  MGC3140 3D Tracking and Gesture Controller Data Sheet
PDF  49 Pages
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Producent  MICROCHIP [Microchip Technology]
Strona internetowa  http://www.microchip.com
Logo MICROCHIP - Microchip Technology

MGC3140 Arkusz danych(HTML) 11 Page - Microchip Technology

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4.
Feature Description
4.1
Gesture Definition
A hand gesture is the movement of the hand to express an idea or meaning. The GestIC technology
accurately allows sensing of a user’s free space hand motion for contact free position tracking, as well as
3D gesture recognition based on classified movement patterns.
4.2
GestIC Library
MGC3140 is being provided with a GestIC Library loader (bootloader) which is stored on the chip’s Flash
memory. Using this loader, a GestIC Library can be flashed on the MGC3140 via I2C using, for example,
an embedded host controller or Microchip's Aurea GUI. The GestIC Library includes:
•
Colibri Suite: Digital Signal Processing (DSP) algorithms and feature implementations.
•
System Control: MGC3140 hardware control.
Related Links
9.1 Aurea Software Package
4.2.1
Colibri Suite
The Colibri Suite combines data acquisition, digital signal processing and interpretation.
The Colibri Suite functional features are illustrated below and described in the following sections.
Figure 4-1. Colibri Suite Core Elements
Digital Signal Processing
Colibri Suite
Position
Tracking
Gesture
Recognition
Approach
Detection
4.2.1.1
Position Tracking
The Colibri Suite’s Position Tracking feature provides 3D hand position over time and area. The absolute
position data is provided according to the defined origin of the Cartesian coordinate system (x, y, z).
Position Tracking data is continuously acquired in parallel to Gesture Recognition. With a position rate of
up to 200 positions/sec., a maximum spatial resolution of 150 dpi is achieved.
4.2.1.2
Gesture Recognition
The Colibri Suite’s gesture recognition model detects and classifies hand movement patterns performed
inside the sensing area.
Using advanced random classification based on Hidden Markov Model (HMM), industry best gesture
recognition rate is being achieved.
MGC3140
Feature Description
© 2018 Microchip Technology Inc.
DS40002037A-page 11



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