Vicini, Marina, Rudorfer, Martin, Dai, Zhuangzhuang and Manso, Luis J. (2024). Integrating Temporal Context into Streaming Data for Human Activity Recognition in Smart Home. IN: Proceedings of the International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2024). Bravo, José; Nugent, Chris and Cleland, Ian (eds) Lecture Notes in Networks and Systems . Springer.
Abstract
With the global population ageing, it is crucial to enable individuals to live independently and safely in their homes. Using ubiq- uitous sensors such as Passive InfraRed sensors (PIR) and door sensors is drawing increasing interest for monitoring daily activities and facilitat- ing preventative healthcare interventions for the elderly. Human Activ- ity Recognition (HAR) from passive sensors mostly relies on traditional machine learning and includes data segmentation, feature extraction, and classification. While techniques like Sensor Weighting Mutual Infor- mation (SWMI) capture spatial context in a feature vector, effectively leveraging temporal information remains a challenge. We tackle this by clustering activities into morning, afternoon, and night, and encoding them into the feature weighting method calculating distinct mutual in- formation matrices. We further propose to extend the feature vector by incorporating time of day and day of week as cyclical temporal features, as well as adding a feature to track the user’s location. The experiments show improved accuracy and F1-score over existing state-of-the-art meth- ods in three out of four real-world datasets, with highest gains in a low- data regime. These results highlight the potential of our approach for developing effective smart home solutions to support ageing in place.
Publication DOI: | https://doi.org/10.1007/978-3-031-77571-0_24 |
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Divisions: | College of Engineering & Physical Sciences > School of Computer Science and Digital Technologies > Applied AI & Robotics College of Engineering & Physical Sciences > Smart and Sustainable Manufacturing College of Engineering & Physical Sciences > Aston Centre for Artifical Intelligence Research and Application College of Engineering & Physical Sciences > School of Computer Science and Digital Technologies College of Engineering & Physical Sciences Aston University (General) |
Additional Information: | Copyright © Springer Nature B.V. 2024. This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://link.springer.com/chapter/10.1007/978-3-031-77571-0_24https://link.springer.com/book/9783031775703 |
Uncontrolled Keywords: | Activity Recognition,Data Segmentation,Mutual Information,Sliding Window,Smart Home,Temporal Context,Control and Systems Engineering,Signal Processing,Computer Networks and Communications |
ISBN: | 9783031775703, 9783031775710 |
Last Modified: | 28 Mar 2025 08:13 |
Date Deposited: | 11 Dec 2024 14:27 |
Full Text Link: | |
Related URLs: |
https://link.sp ... -031-77571-0_24
(Publisher URL) http://www.scop ... tnerID=8YFLogxK (Scopus URL) |
PURE Output Type: | Conference contribution |
Published Date: | 2024-12-21 |
Published Online Date: | 2024-12-20 |
Accepted Date: | 2024-10-27 |
Authors: |
Vicini, Marina
Rudorfer, Martin ( ![]() Dai, Zhuangzhuang ( ![]() Manso, Luis J. ( ![]() |