University of Oulu

Nawal, Y., Oussalah, M., Fergani, B. et al. New incremental SVM algorithms for human activity recognition in smart homes. J Ambient Intell Human Comput 14, 13433–13450 (2023).

New incremental SVM algorithms for human activity recognition in smart homes

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Author: Nawal, Yala1; Oussalah, Mourad2; Fergani, Belkacem1;
Organizations: 1LISIC Laboratory, Electronics and Computer Sciences Department, University of Science and Technology Houari Boumediene, Algiers 16000, Algeria
2Faculty of ITEE, CMVS, University of Oulu, PO Box 4500, Oulu 90014, North Ostrobothnia, Finland
3Computer Science and Automatic Control Department, University of Lille, IMT LIlle Douai, Lille 5900, Nord, France
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2.9 MB)
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Language: English
Published: Springer Nature, 2023
Publish Date: 2023-10-13


Smart homes are equipped with several sensor networks to keep an eye on both residents and their environment, to interpret the current situation and to react immediately. Handling large scale dataset of sensory events on real time to enable efficient interventions is challenging and very difficult. To deal with these data flows and challenges, traditional streaming data classification approaches can be boosted by use of incremental learning. In this paper, we presented two new Incremental SVM methods to improve the performance of SVM classification in the context of human activity recognition tasks. Two feature extraction methods elaborated by refining dependency sensor extraction feature and focusing on the last sensor event only have been suggested. On the other hand, a clustering based approach and a similarity based approach have been suggested to boost learning performance of the incremental SVM algorithms capitalizing on the relationship between data chunk and support vectors of previous chunk. We demonstrate through several simulations on two major publicly available data sets (Aruba and Tulum), the feasibility and improvements in learning and classification performances in real time achieved by our proposed methods over the state-of-the-art. For instance, we have shown that the introduced similarity-based incremental learning is 5 to 9 times faster than other methods in terms of training performances. Similarly, the introduced Last-state sensor feature method induces at least 5% improvement in terms of F1-score when using baseline SVM classifier.

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Series: Journal of ambient intelligence and humanized computing
ISSN: 1868-5137
ISSN-E: 1868-5145
ISSN-L: 1868-5137
Volume: 14
Pages: 13433 - 13450
DOI: 10.1007/s12652-022-03798-w
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: This work is partly supported by European project YoungRes (#823701), and Academy of Finland DigiHealth projects No.326291 which are gratefully acknowledged.
Dataset Reference: All employed dataset are open sources.
Copyright information: © The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit