Passive wireless sensing for unsupervised human activity recognition in healthcare

Wenda Li, Yangdi Xu, Bo Tan, Robert Piechocki

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

15 Citations (Scopus)
348 Downloads (Pure)


Physical activity classification is an important tool for various applications such as activity of daily living (ADL) recognition and fall detection. Additionally, the non-contact nature of radar systems provides minimally invasive sensing platform. Doppler-based radar has been used for activity classification in the past. However, most of these studies considered supervised classification which requires labeled training data sets. In this paper, we propose a novel procedure of using micro Doppler radar for unsupervised classification with Hidden Markov Models (HMM). A low-complexity time alignment method for capturing activity is developed and an Elbow test has been adopted for model selection. Test results confirm the efficacy of the selected feature set and the proposed methodology. The results prove the proposed system can deliver a very good performance in ADL recognition tasks.
Original languageEnglish
Title of host publication2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC 2017)
Subtitle of host publicationProceedings of a meeting held 26-30 June 2017, Valencia, Spain
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages1533
ISBN (Electronic)9781509043729
ISBN (Print)9781509043736
Publication statusPublished - Sept 2017

Publication series

ISSN (Print)2376-6506


  • Passive Sensing
  • Doppler Radar
  • Human Activity Recognition
  • Unsupervised Classification
  • HMM


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