A Hybrid Discriminative/Generative Approach for Modeling Human Activities
Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence. Pages 766-771.
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%.
@inproceedings{ijcai2005p101,
title = {A Hybrid Discriminative/Generative Approach for Modeling Human Activities},
author = {Lester, Jonathan and Choudhury, Tanzeem and Kern, Nicky and Borriello, Gaetano and Hannaford, Blake},
booktitle = {Proceedings of the Nineteenth International Joint Conference on
Artificial Intelligence, {IJCAI-05}},
publisher = {International Joint Conferences on Artificial Intelligence},
pages = {766--771},
year = {2005},
url = {https://ijcai.org/proceedings/2005/101},
}
TY - CPAPER TI - A Hybrid Discriminative/Generative Approach for Modeling Human Activities AU - Lester, Jonathan AU - Choudhury, Tanzeem AU - Kern, Nicky AU - Borriello, Gaetano AU - Hannaford, Blake T2 - Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05 PB - International Joint Conferences on Artificial Intelligence PY - 2005 SP - 766 EP - 771 UR - https://ijcai.org/proceedings/2005/101 ER -Copied
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