Human activity learning for assistive robotics using a classifier ensemble

Adama, D.A. ORCID: 0000-0002-2650-857X, Lotfi, A. ORCID: 0000-0002-5139-6565, Langensiepen, C. ORCID: 0000-0002-0165-9048, Lee, K. ORCID: 0000-0002-2730-9150 and Trindade, P., 2018. Human activity learning for assistive robotics using a classifier ensemble. Soft Computing: a Fusion of Foundations, Methodologies and Applications, 22 (21), pp. 7027-7039. ISSN 1432-7643

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Assistive robots in ambient assisted living environments can be equipped with learning capabilities to effectively learn and execute human activities. This paper proposes a human activity learning (HAL) system for application in assistive robotics. An RGB-depth sensor is used to acquire information of human activities, and a set of statistical, spatial and temporal features for encoding key aspects of human activities are extracted from the acquired information of human activities. Redundant features are removed and the relevant features used in the HAL model. An ensemble of three individual classifiers—support vector machines (SVMs), K-nearest neighbour and random forest - is employed to learn the activities. The performance of the proposed system is improved when compared with the performance of other methods using a single classifier. This approach is evaluated on experimental dataset created for this work and also on a benchmark dataset—the Cornell Activity Dataset (CAD-60). Experimental results show the overall performance achieved by the proposed system is comparable to the state of the art and has the potential to benefit applications in assistive robots for reducing the time spent in learning activities.

Item Type: Journal article
Publication Title: Soft Computing: a Fusion of Foundations, Methodologies and Applications
Creators: Adama, D.A., Lotfi, A., Langensiepen, C., Lee, K. and Trindade, P.
Publisher: Springer
Date: November 2018
Volume: 22
Number: 21
ISSN: 1432-7643
Rights: © The Author(s) 2018. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecomm, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Divisions: Schools > School of Science and Technology
Record created by: Linda Sullivan
Date Added: 23 Jul 2018 13:10
Last Modified: 21 Jan 2021 11:25

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