Human activity recognition using tag-based localization
Author
Yurtman, Aras
Barshan, Barshan
Date
2012-04Source Title
Proceedings of the 20th IEEE Conference on Signal Processing and Communications Applications, 2012
Publisher
IEEE
Language
Turkish
Type
Conference PaperItem Usage Stats
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Show full item recordAbstract
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.
Keywords
Classification methodsClassification technique
Comparative studies
Cross-validation technique
Human activities
Human activity recognition
Localization system
Radio frequencies
Tag-based
Signal processing
Curve fitting