Integrating Temporal Context into Streaming Data for Human Activity Recognition in Smart Home

Research output: Chapter in Book/Published conference outputConference publication

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.
Original languageEnglish
Title of host publicationProceedings of the International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2024)
EditorsJosé Bravo, Chris Nugent, Ian Cleland
PublisherSpringer
Pages238-251
Number of pages14
ISBN (Electronic)9783031775710
ISBN (Print)9783031775703
DOIs
Publication statusPublished - 21 Dec 2024

Publication series

NameLecture Notes in Networks and Systems
Volume1212 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Bibliographical note

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

Keywords

  • Activity Recognition
  • Data Segmentation
  • Mutual Information
  • Sliding Window
  • Smart Home
  • Temporal Context

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