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[198] Citation: J. Lester, T. Choudhury, N. Kern, G. Borriello, B. Hannaford,
'A Hybrid Discriminative/Generative Approach for Modeling Human Activities,'
Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, pp. 766 - 722, Edinburgh, Scotland, 2005.
Abstract
Accurate recognition and tracking of human activities is an important
goal of ubiquitous computing. Recent advances in the development of
multi-modal wearable sensors enable us to gather rich datasets of human
activities. However, the problem of automatically identifying the most
useful features for modeling such activities remains largely unsolved.
In this paper we present a hybrid approach to recognizing activities,
which combines boosting to discriminatively select useful features and
learn an ensemble of static classifiers to recognize different
activities, with hidden Markov models (HMMs) to capture the temporal
regularities and smoothness of activities. We tested the activity
recognition system using over 12 hours of wearable-sensor data collected
by volunteers in natural unconstrained environments. The models
succeeded in identifying a small set of maximally informative features,
and were able identify ten different human activities with an accuracy
of 95%.
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Updated: Tue Aug 19 09:16:09 2008
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