A Human Activity Recognition Framework for Healthcare Applications: ontology, labelling strategies, and best practice

Przemyslaw R Woznowski, Rachel King, William Harwin, Ian Craddock

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

13 Citations (Scopus)
489 Downloads (Pure)

Abstract

Human Activity Recognition (AR) is an area of great importance for health and well-being applications including Ambient Intelligent (AmI) spaces, Ambient Assisted Living (AAL) environments, and wearable healthcare systems. Such intelligent systems reason over large amounts of sensor-derived data in order to recognise users’ actions. The design of AR algorithms relies on ground-truth data of sufficient quality and quantity to enable rigorous training and validation. Ground-truth is often acquired using video recordings which can produce detailed results given the appropriate labels. However, video annotation is not a trivial task and is, by definition, subjective. In addition, the sensitive nature of the recordings has to be foremost in minds of the researchers to protect the identity and privacy of participants. In this paper, a hierarchical ontology for the annotation of human activity recognition in the home is proposed. Strategies that support different levels of granularity are presented enabling consistent, and repeatable annotations for training and validating activity recognition algorithms. Best practice regarding the handling of this type of sensitive data is discussed.
Original languageEnglish
Title of host publicationProceedings of the International Conference on Internet of Things and Big Data 2016
Place of PublicationRome, Italy
PublisherSciTePress
Pages369-377
Number of pages9
ISBN (Electronic)9789897581830
DOIs
Publication statusPublished - 23 Apr 2016
EventInternational Conference on Internet of Things and Big Data, IoTBD 2016 - Rome, Italy
Duration: 23 Apr 201625 Apr 2016

Conference

ConferenceInternational Conference on Internet of Things and Big Data, IoTBD 2016
Abbreviated titleIoTBD 2016
CountryItaly
CityRome
Period23/04/1625/04/16

Structured keywords

  • Digital Health

Keywords

  • Activity Recognition
  • Annotation
  • Ontology
  • Video

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