Projects

Phase 2 Projects

Organic Computing Techniques for Run-Time Self-Adaptation of Multi-Modal Activity Recognition Systems


Project leaders: Bernhard Sick (Kassel), Paul Lukowicz (Kaiserslautern)

Researchers: Sven Tomforde (Kassel), Martin Jänicke (Kassel)

Administration:

Associates:

Summary:
Ubiquitous activity and context recognition (AR) aims at translating
information provided by simple sensors into high level knowledge about
human activities and the situation in the environment. Over the last
decade researchers have shown the principle feasibility of recognizing
activities and situations ranging from the steps of a maintenance
task, through every day activities at home, to sport and social
interactions. A major limitation of today's state of the art
approaches is that they mostly assume system configurations exactly
defined at the system’s design-time that remain fixed at run-time.
Thus, for each application, the user needs to place specific sensors
at certain well-defined locations in the environment and on his body.
All stages of the signal processing chain (from signal conditioning
through feature selection to classification) are then custom-designed
for the concrete task. While such static runtime setups can be
guaranteed under controlled laboratory conditions, the possibility of
sensors dropping out and new sensors appearing must be taken into
account in real world settings. In this proposal we will develop new
Organic Computing (OC) techniques to facilitate self-healing (when a
sensor drops out) and self-improvement (when a new sensor appears) for
ubiquitous activity and context recognition systems. Specifically, we
will develop a layered Observer/Controller architecture where the
System under Observation and Control (SuOC) is (are) human(s) in an
intelligent, sensor enabled environment. The bottom layer (reaction
layer) can be seen as a blueprint of standard AR based context
sensitive systems. The adaptation layer enables the system to improve
autonomously -- or semi-autonomously with sporadic human feedback --
the classifier at the reaction layer using the new sensor information
or to adapt it if a sensor drops out. In general, autonomous
adaptation methods cannot guarantee to always lead to an improvement
and, in special cases, they can even result in performance
degradation. Thus, the potential gains and the risks of a possible
adaptation are estimated and considered not only at the adaptation
layer, but also at the reflection layer (top layer) that models the
long term system evolution to ensure that continuous modifications of
the system configuration lead to long term improvement and not to
un-bounded performance degradation of the overall system. In our
approach we develop new OC techniques for AR by combining and
extending methods from Machine Leaning, Pattern Recognition, and
related fields (in particular generative and discriminative modeling,
semi-supervised learning, active learning, and nonlinear dynamic
systems theory). We will evaluate our methods on existing large scale
AR data sets.

Phase 1 Projects

Organic Computing Techniques for Run-Time Self-Adaptation of Multi-Modal Activity Recognition Systems


Project leaders: Bernhard Sick (Kassel), Paul Lukowicz (Kaiserslautern)

Researchers: Sven Tomforde (Kassel), Martin Jänicke (Kassel)

Administration:

Associates:

Summary:
Ubiquitous activity and context recognition (AR) aims at translating
information provided by simple sensors into high level knowledge about
human activities and the situation in the environment. Over the last
decade researchers have shown the principle feasibility of recognizing
activities and situations ranging from the steps of a maintenance
task, through every day activities at home, to sport and social
interactions. A major limitation of today's state of the art
approaches is that they mostly assume system configurations exactly
defined at the system’s design-time that remain fixed at run-time.
Thus, for each application, the user needs to place specific sensors
at certain well-defined locations in the environment and on his body.
All stages of the signal processing chain (from signal conditioning
through feature selection to classification) are then custom-designed
for the concrete task. While such static runtime setups can be
guaranteed under controlled laboratory conditions, the possibility of
sensors dropping out and new sensors appearing must be taken into
account in real world settings. In this proposal we will develop new
Organic Computing (OC) techniques to facilitate self-healing (when a
sensor drops out) and self-improvement (when a new sensor appears) for
ubiquitous activity and context recognition systems. Specifically, we
will develop a layered Observer/Controller architecture where the
System under Observation and Control (SuOC) is (are) human(s) in an
intelligent, sensor enabled environment. The bottom layer (reaction
layer) can be seen as a blueprint of standard AR based context
sensitive systems. The adaptation layer enables the system to improve
autonomously -- or semi-autonomously with sporadic human feedback --
the classifier at the reaction layer using the new sensor information
or to adapt it if a sensor drops out. In general, autonomous
adaptation methods cannot guarantee to always lead to an improvement
and, in special cases, they can even result in performance
degradation. Thus, the potential gains and the risks of a possible
adaptation are estimated and considered not only at the adaptation
layer, but also at the reflection layer (top layer) that models the
long term system evolution to ensure that continuous modifications of
the system configuration lead to long term improvement and not to
un-bounded performance degradation of the overall system. In our
approach we develop new OC techniques for AR by combining and
extending methods from Machine Leaning, Pattern Recognition, and
related fields (in particular generative and discriminative modeling,
semi-supervised learning, active learning, and nonlinear dynamic
systems theory). We will evaluate our methods on existing large scale
AR data sets.