Human activity recognition using tag-based localization

Date
2012-04
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Source Title
Proceedings of the 20th IEEE Conference on Signal Processing and Communications Applications, 2012
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Publisher
IEEE
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Language
Turkish
Type
Conference Paper
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Abstract

This paper provides a comparative study on the different techniques of classifying human activities using a tag-based radio-frequency (RF) localization system. Non-uniformly-sampled data containing position measurements of the tags on the body is first converted to a uniformly-sampled one using different curve-fitting algorithms. Then, the data is partitioned into segments. Finally, various classification techniques are applied to classify human activities. Curve-fitting, segmentation, and classification methods are compared using different cross-validation techniques and the combination resulting in the best performance is presented. The results indicate that the system demonstrates acceptable performance despite the fact that tag-based RF localization is not very accurate.

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Keywords
Classification methods, Classification technique, Comparative studies, Cross-validation technique, Human activities, Human activity recognition, Localization system, Radio frequencies, Tag-based, Signal processing, Curve fitting
Citation
Published Version (Please cite this version)