Recognition of unscripted kitchen activities and eating behaviour for health monitoring

Sam Whitehouse, Kristina Yordanova, Adeline Paiement, Majid Mirmehdi

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

8 Citations (Scopus)
333 Downloads (Pure)

Abstract

Nutrition related health conditions such as diabetes and obesity can seriously impact quality of life for those who are affected by them. A system able to monitor kitchen activities and patients’ eating behaviours could provide clinicians with important information helping them to improve patients’ treatments. We propose a symbolic model able to describe unscripted kitchen activities and eating habits of people in home settings. This model consists of an ontology which describes the problem domain, and a Computational State Space Model (CSSM) which is able to reason in a probabilistic manner about a subject’s actions, goals, and causes of any problems during task execution. To validate our model we recorded 15 unscripted kitchen activities involving 9 subjects, with the video data being annotated according to the proposed ontology schemata. We then evaluated the model’s ability to recognise activities and potential goals from action sequences by simulating noisy observations from the annotations. The results showed that our model is able to recognise kitchen activities with an average accuracy of 80% when using specialised models, and with an average accuracy of 40% when using the general model.
Original languageEnglish
Title of host publication2nd IET International Conference on Technologies for Active and Assisted Living (TechAAL 2016)
Subtitle of host publicationProceedings of a meeting held 24-25 October 2016, London, UK
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-6
Number of pages6
ISBN (Electronic)9781785613937
ISBN (Print)9781510834699
DOIs
Publication statusPublished - Feb 2017

Structured keywords

  • Digital Health

Keywords

  • Activity recognition
  • Intention recognition
  • Kitchen activities
  • Digital Health
  • Unscripted activities

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