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Research at St Andrews

Representation learning for minority and subtle activities in a smart home environment

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Daily human activity recognition using sensor data can be a fundamental task for many real-world applications, such as home monitoring and assisted living. One of the challenges in human activity recognition is to distinguish activities that have infrequent occurrence and less distinctive patterns. We propose a dissimilarity representation-based hierarchical classifier to perform two-phase learning. In the first phase, the classifier learns general features to recognise majority classes, and the second phase is to collect minority and subtle classes to identify fine difference between them. We compare our approach with a collection of state-of-the-art classification techniques on a real-world third-party dataset that is collected in a two-user home setting. Our results demonstrate that our hierarchical classifier approach outperforms the existing techniques in distinguishing users in performing the same type of activities. The key novelty of our approach is the exploration of dissimilarity representations and hierarchical classifiers, which allows us to highlight the difference between activities with subtle difference, and thus allows the identification of well-discriminating features.
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Details

Original languageEnglish
Title of host publicationProceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom 2019)
PublisherIEEE Computer Society
Number of pages7
ISBN (Electronic)9781538691489, 9781538691472
ISBN (Print)9781538691496
DOIs
Publication statusPublished - 22 Jul 2019
EventIEEE International Conference on Pervasive Computing and Communications (PerCom 2019) - Kyoto, Japan
Duration: 12 Mar 201914 Mar 2019
Conference number: 17
http://www.percom.org/Previous/ST2019/home.html

Conference

ConferenceIEEE International Conference on Pervasive Computing and Communications (PerCom 2019)
Abbreviated titlePerCom 2019
CountryJapan
CityKyoto
Period12/03/1914/03/19
Internet address

    Research areas

  • Smart home, Activity recognition, Dissimilarity representation, Representation learning

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ID: 260282913

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