NDLI: Ambient living activity recognition based on feature-set ranking using intelligent systems

Content Provider	IEEE Xplore Digital Library
Author	Baños, O. Pomares, H. Rojas, I.
Copyright Year	2010
Description	Author affiliation: Department of Computer Architecture and Computer Technology, University of Granada, 18071 SPAIN (Baños, O.; Pomares, H.; Rojas, I.)
Abstract	E-health and e-monitoring have become an increasingly important area during recent years, being the recognition of motion, postures and physical exercises one of the main topics. In this kind of problem is common to work with a huge training data set in a multidimensional space, so feature selection is absolutely necessary. Most works are based on knowledge extraction using features which permit to make decisions about the activity realized, being feature selection the most critical stage. Conventional feature selection procedures based on wrapper methods or ‘branch and bound’ are highly computationally expensive. In this work, we propose an alternative filter method using a feature-set ranking via a couple of two statistical criteria, which achieves remarkable accuracy rates in the classification process. We demonstrate the usefulness of our method on both laboratory and seminaturalistic activity ambient living datasets for real problems.
Starting Page	1
Ending Page	4
File Size	695845
Page Count	4
File Format	PDF
ISBN	9781424469161
ISSN	10987576
e-ISBN	9781424469185
DOI	10.1109/IJCNN.2010.5596635
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-18
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Accelerometers Sensors Robustness Legged locomotion Laboratories Acceleration
Content Type	Text
Resource Type	Article

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