Rule-Based Real-Time ADL Recognition in a Smart Home Environment

G Baryannis, Przemyslaw R Woznowski, Gregoris Antoniou

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

10 Citations (Scopus)
346 Downloads (Pure)


This paper presents a rule-based approach for both offline and real-time recognition of Activities of Daily Living (ADL), leveraging events produced by a non-intrusive multi-modal sensor infrastructure deployed in a residential environment. Novel aspects of the approach include: the ability to recognise arbitrary scenarios of complex activities using bottom-up multi-level reasoning, starting from sensor events at the lowest level; an effective heuristics-based method for distinguishing between actual and ghost images in video data; and a highly accurate indoor localisation approach that fuses different sources of location information. The proposed approach is implemented as a rule-based system using Jess and is evaluated using data collected in a smart home environment. Experimental results show high levels of accuracy and performance, proving the effectiveness of the approach in real world setups.
Original languageEnglish
Title of host publicationRule Technologies. Research, Tools, and Applications
Subtitle of host publication10th International Symposium, RuleML 2016, Stony Brook, NY, USA, July 6-9, 2016. Proceedings
EditorsJose Julio Alferes, Leopoldo Bertossi, Guido Governatori, Paul Fodor, Dumitru Roman
Number of pages16
ISBN (Electronic)9783319420196
ISBN (Print)9783319420189
Publication statusPublished - 2 Aug 2016

Publication series

NameLecture Notes in Computer Science
PublisherSpringer International Publishing
ISSN (Print)0302-9743

Structured keywords

  • Digital Health


  • Event driven architectures
  • Activity recognition
  • ADL
  • Indoor localisation
  • Smart home
  • Multi-modal sensing


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