Passive Wireless Sensing for Unsupervised Human Activity Recognition in Healthcare

Wenda Li, Bo Tan, Yangdi Xu, Robert Piechocki

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

5 Citations (Scopus)

Abstract

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 publication 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC)
PublisherIEEE
Pages1528-1533
ISBN (Electronic)978-1-5090-4372-9
ISBN (Print)978-1-5090-4373-6
Publication statusPublished - 20 Jul 2017
EventInternational Wireless Communications and Mobile Computing Conference - Holiday Inn, Valencia, Spain
Duration: 26 Jun 201730 Jun 2017
Conference number: 13
http://iwcmc.org/2017/ (Link to the conference website)

Conference

ConferenceInternational Wireless Communications and Mobile Computing Conference
Abbreviated titleIWCMC2017
CountrySpain
CityValencia
Period26/06/1730/06/17
Internet address

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  • Research Output

    • 5 Citations
    • 1 Chapter

    SPHERE: A sensor platform for healthcare in a residential environment

    Woznowski, P., Burrows, A., Diethe, T., Fafoutis, X., Hall, J., Hannuna, S., Camplani, M., Twomey, N., Kozlowski, M., Tan, B., Zhu, N., Elsts, A., Vafeas, A., Paiement, A., Tao, L., Mirmehdi, M., Burdhardt, T., Damen, D., Flach, P., Piechocki, R. & 2 others, Craddock, I. & Oikonomou, G., 6 Dec 2016, Designing, Developing, and Facilitating Smart Cities: Urban Design to IoT Solutions. Angelakis, V., Tragos, E., Pöhls, H. C., Kapovits, A. & Bassi, A. (eds.). Switzerland: Springer, p. 315-333 19 p.

    Research output: Chapter in Book/Report/Conference proceedingChapter

  • Cite this

    Li, W., Tan, B., Xu, Y., & Piechocki, R. (2017). Passive Wireless Sensing for Unsupervised Human Activity Recognition in Healthcare. In 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC) (pp. 1528-1533). IEEE.