MAGI System - Mind Augmented Gesture Interaction
MAGI is a research project, part of a collaboration between the MISG
group at EIA-FR and the DIVA group at UNIFR.
The interaction between Human-Environment is been always tied to the society
technological level. With the explosion of electronics and computer science
that habits are changed more rapidly than ever before. This new aspect
takes the name of Human-Computer-Environment interaction. That has caused
the rise of specialized branches of research analyzing new and more natural
ways of control, communication and new methods to give to computers awareness
about the ``context''.
In order to make human-computer interfaces truly natural, we need to develop
technology that tracks human movement, body behavior and facial expression,
and interprets these gestures in an affective way. The gesture-based interaction
is a natural way to interact and can represent a substitution/complement
for other forms of communications for impaired people or in special contexts.
The usual approaches to address the gesture recognition challenges go
into the direction of computer vision and image processing. These methods
are limited by some typical environmental constraints such as sensitivity
to lighting changes, reliance on camera position, etc.; moreover, the
images elaboration is very expensive in terms of computer processing power,
making the real-time analysis a very difficult challenge.
On the other hand, the increasing centrality of the human being inside
the design of new systems requires considering the most important of ``contexts'':
the human being himself. In this direction psycho-physiological sensors
such as Electroencephalogram (EEG), Electromyogram (EMG), Blood Volume
Pressure (BVP) or Galvanic Skin Response (GSR) (just to mention the well-known
technologies) give important information about the cognitive, affective
or health subject's conditions.
In this work we interlace the concepts of Gesture Recognition, Context
Awareness and Multimodal Interaction based on psycho-physiological signals
in order to enhance our communication and control capabilities with the
everyday object of our life, and overcome the obstacles present in the
Computer Vision approach.