Chinellato, E, Hogg, DC orcid.org/0000-0002-6125-9564 and Cohn, AG orcid.org/0000-0002-7652-8907 (2016) Feature space analysis for human activity recognition in smart environments. In: 12th International Conference on Intelligent Environments (IE 2016). 12th International Conference on Intelligent Environments (IE 2016), 14-16 Sep 2016, London, UK. IEEE , pp. 194-197. ISBN 978-1-5090-4056-8
Abstract
Activity classification from smart environment data is typically done employing ad hoc solutions customised to the particular dataset at hand. In this work we introduce a general purpose collection of features for recognising human activities across datasets of different type, size and nature. The first experimental test of our feature collection achieves state of the art results on well known datasets, and we provide a feature importance analysis in order to compare the potential relevance of features for activity classification in different datasets.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Feature extraction; Time measurement; Testing; Indexes; Complexity theory |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 25 May 2017 12:45 |
Last Modified: | 16 Jan 2018 07:12 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/IE.2016.43 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:116769 |