Using model-based clustering to discretise duration information for activity recognition

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

3 Citations (Scopus)

Abstract

Activity recognition is an important component of patient management in smart homes where high level activities can be learned from low level sensor data. Such activity recognition utilises sensor ID, task order and time of activation to learn about patient behavior, detect anomalies and provide prompts or other interventions. In this paper we use the sensor activation times to calculate durations and then investigate several model-based clustering approaches with a view to discretising the duration data and using such data to improve activity prediction. We explore several popular approaches to characterising such duration data, namely Coxian phase type distributions and Gaussian mixture distributions. We then show how we can utilise the learned clustering components for discretisation. Finally we use simulated data, based on a real smart kitchen deployment, to compare these approaches and evaluate the discretisation results with regard to activity prediction.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherIEEE Computer Society
Pages1-7
Number of pages7
VolumeCBMS 2
ISBN (Print)978-1-4577-1189-3
DOIs
Publication statusPublished - 2011
EventProceedings of the 24th IEEE International Symposium on Computer-Based Medical Systems, 27-30 June, 2011, Bristol, United Kingdom - Bristol
Duration: 1 Jan 2011 → …

Conference

ConferenceProceedings of the 24th IEEE International Symposium on Computer-Based Medical Systems, 27-30 June, 2011, Bristol, United Kingdom
Period1/01/11 → …

Keywords

  • Activity recognition
  • Duration

Fingerprint Dive into the research topics of 'Using model-based clustering to discretise duration information for activity recognition'. Together they form a unique fingerprint.

  • Cite this