Evolving discriminative features robust to sensor displacement for activity recognition in body area sensor networks
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @InProceedings{Forster:2009:ISSNIP,
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author = "Kilian Forster and Pascal Brem and Daniel Roggen and
Gerhard Troster",
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title = "Evolving discriminative features robust to sensor
displacement for activity recognition in body area
sensor networks",
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booktitle = "5th International Conference on Intelligent Sensors,
Sensor Networks and Information Processing, ISSNIP
2009",
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year = "2009",
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month = dec,
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pages = "43--48",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/ISSNIP.2009.5416810",
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abstract = "Activity and gesture recognition from body-worn
acceleration sensors is an important application in
body area sensor networks. The key to any such
recognition task are discriminative and variation
tolerant features. Furthermore good features may reduce
the energy requirements of the sensor network as well
as increase the robustness of the activity recognition.
We propose a feature extraction method based on genetic
programming. We benchmark this method using two
datasets and compare the results to a feature selection
which is typically used for obtaining a set of
features. With one extracted feature we achieve an
accuracy of 73.4percent on a fitness activity dataset,
in contrast to 70.1percent using one selected standard
feature. In a gesture based HCI dataset we achieved
95.0percent accuracy with one extracted feature. A
selection of up to five standard features achieved
90.6percent accuracy in the same setting. On the HCI
dataset we also evaluated the robustness of extracted
features to sensor displacement which is a common
problem in movement based activity and gesture
recognition. With one extracted features we achieved an
accuracy of 85.0percent on a displaced sensor position.
With the best selection of standard features we
achieved 55.2percent accuracy. The results show that
our proposed genetic programming feature extraction
method is superior to a feature selection based on
standard features.",
-
notes = "Also known as \cite{5416810}",
- }
Genetic Programming entries for
Kilian Forster
Pascal Brem
Daniel Roggen
Gerhard Troster
Citations