Ultrasonic circular array sensing for human arm motion classification

R. Van Sloun, A. Pandharipande, D. Caicedo, S. Srinivasan, P. Sommen

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Downloads (Pure)

Abstract

Classification of human arm movements is an important problem in healthcare and well-being applications. In this paper, an ultrasonic Uniform Circular Array (UCA) Doppler sensing method is proposed for classifying arm movements from a given set. The method uses velocity and angular information, derived from Doppler frequencies and direction-of-arrival (DoA) by processing the signals received at the UCA, and employs a Bayesian classifier to distinguish between movements. The performance of the sensing method is evaluated using experimental datasets. The proposed ultrasonic UCA Doppler sensor and processing methods provide a compact solution for human arm movement classification.

Original languageEnglish
Title of host publicationConsumer Electronics – Berlin (ICCE-Berlin), 2014 IEEE Fourth International Conference, Berlin, 7-10 September 2014
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages24-25
Number of pages2
ISBN (Electronic)978-1-4799-6165-8
DOIs
Publication statusPublished - 5 Feb 2015

Keywords

  • Human arm movement classification
  • UCA Doppler sensor

Fingerprint Dive into the research topics of 'Ultrasonic circular array sensing for human arm motion classification'. Together they form a unique fingerprint.

Cite this