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SLearn: shared learning human activity labels across multiple datasets

Research output: ResearchConference contribution

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Abstract

The research of sensor-based human activity recognition has been attracting increasing attention over years as it is playing an important role in various human-beneficiary applications such as ambient assistive living, health monitoring, and behaviour changing. Nowadays, the advancement of sensing and communication technologies has led to the possibility of collecting a large amount of sensor data, however, to build a reliable computational model and accurately recognise human activities we still need the annotations on sensor data. Acquiring high-quality, detailed, continuous annotations is a challenging task. In this paper, we explore the solution space on sharing annotated activities across different datasets in order to enhance the recognition accuracies. We have designed and developed two approaches: sharing training data and sharing classifiers towards addressing this challenge. We have validated the approach on three datasets and demonstrated their effectiveness in recognising activities only with annotations from as little as 0.1% of each dataset.
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Original languageEnglish
Title of host publicationIEEE International Conference on Pervasive Computing and Communications
PublisherIEEE Computer Society
Number of pages10
StatePublished - 19 Mar 2018
EventIEEE International Conference on Pervasive Computing and Communications (PerCom) - Divani Caravel Hotel, Athens, Greece
Duration: 19 Mar 201823 Mar 2018
http://www.percom.org/

Conference

ConferenceIEEE International Conference on Pervasive Computing and Communications (PerCom)
Abbreviated titlePerCom
CountryGreece
CityAthens
Period19/03/1823/03/18
Internet address

    Research areas

  • Human activity recognition, Smart home, Active learning, Transfer learning, Uncertainty reasoning

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ID: 251864837