Description
Date depot: 1 janvier 1900
Titre: Embedded architecture and physiological sensors
Directrice de thèse:
Maria RIFQI (LEMA)
Directeur de thèse:
Christophe MARSALA (LIP6)
Domaine scientifique: Sciences et technologies de l'information et de la communication
Thématique CNRS : Non defini
Resumé:
The originality of this thesis is the design of the most appropriate embedded architect
ure
implementing dynamic learning techniques on physiological signals (EDA, EEG, ECG,
EMG ...)
to
automatically recognize emotions. The objective is to obtain an architecture that reacts as closer as
possible to a particular person. For this, the machine
learning algorithm must automatically adapt to
new physiological data it receives to implement automatic recognition of a mental state. The automatic
adaptation of the learning algorithm to these changes is an emerging problem and challenges of the
thesis
are to design algorithms and architectures: embedded, effective execution speed and memory
space; capable of integrating new descriptors, as well as new classes (new mental states); capable of
detecting abrupt changes or breaks without confusing them with
noise; able to follow developments
and remaining robust and therefore knowing control oblivion. These challenges will be validated in
several scenarios, such as video games, coaching, professional training and events.
Connection to the SENSE project:
The purpose of this thesis is to study emotions and learning in human interactions
-
virtual agent. It is
to apply the concepts of extraction and characterization of social signals (WP2 SENSE project) and
adaptation over time interaction (WP4) to determine th
e emotional impact of sequences successive one
video game, a coaching session or training, a person, to fit the scenario of the game, or the sequencing
of the coaching session or training accordingly.
Doctorant.e: Yang Wenlu