Feature space analysis for human activity recognition in smart environments

Chinellato, Eris ORCID logoORCID: https://orcid.org/0000-0003-1920-2238, Hogg, David C. and Cohn, Anthony G. (2016) Feature space analysis for human activity recognition in smart environments. 2016 12th International Conference on Intelligent Environments (IE). In: 12th International Conference on Intelligent Environments (IE), 14-16 Sept 2016, London, United Kingdom. ISBN 9781509040568. ISSN 2472-7571 [Conference or Workshop Item] (doi:10.1109/IE.2016.43)

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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.

Item Type: Conference or Workshop Item (Poster)
Research Areas: A. > School of Science and Technology
Item ID: 23823
Notes on copyright: © 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.
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Depositing User: Eris Chinellato
Date Deposited: 08 Mar 2018 10:04
Last Modified: 29 Nov 2022 21:35
URI: https://eprints.mdx.ac.uk/id/eprint/23823

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