Activity recognition based on micro-Doppler signature with in-home Wi-Fi

Qingchao Chen, Bo Tan, Kevin Chetty, Karl Woodbridge

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

19 Citations (Scopus)

Abstract

Device free activity recognition and monitoring has become a promising research area with increasing public interest in pattern of life monitoring and chronic health conditions. This paper proposes a novel framework for in-home Wi-Fi signal-based activity recognition in e-healthcare applications using passive micro-Doppler (m-D) signature classification. The framework includes signal modeling, Doppler extraction and m-D classification. A data collection campaign was designed to verify the framework where six m-D signatures corresponding to typical daily activities are sucessfully detected and classified using our software defined radio (SDR) demo system. Analysis of the data focussed on potential discriminative characteristics, such as maximum Doppler frequency and time duration of activity. Finally, a sparsity induced classifier is applied for adaptting the method in healthcare application scenarios and the results are compared with those from the well-known Support Vector Machine (SVM) method.

Original languageEnglish
Title of host publication2016 IEEE 18th International Conference on e-Health Networking, Applications and Services, Healthcom 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)978-1-5090-3370-6
ISBN (Print)978-1-5090-3371-3
DOIs
Publication statusPublished - 21 Nov 2016
Externally publishedYes
Event18th IEEE International Conference on e-Health Networking, Applications and Services - Munich, Germany
Duration: 14 Sep 201617 Sep 2016

Conference

Conference18th IEEE International Conference on e-Health Networking, Applications and Services
Abbreviated titleHealthcom 2016
CountryGermany
CityMunich
Period14/09/1617/09/16

Keywords

  • Activity Recognition
  • micro-Doppler signature
  • Passive Wi-Fi radar
  • sparsity induced classification

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Networks and Communications
  • Health(social science)
  • Health Informatics

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  • Cite this

    Chen, Q., Tan, B., Chetty, K., & Woodbridge, K. (2016). Activity recognition based on micro-Doppler signature with in-home Wi-Fi. In 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services, Healthcom 2016 [7749457] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/HealthCom.2016.7749457