Fielded Autonomous Posture Classification Systems: Design and Realistic Evaluation

Ramona Rednic, John Kemp, Elena Gaura, James Brusey

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Citations (Scopus)
33 Downloads (Pure)

Abstract

Few Body Sensor Network (BSN) based posture classification systems have been fielded to date, despite laboratory based research work confirming their theoretical suitability for a range of applications. This paper reports and reflects on two algorithms which i) improve the accuracy of real-time, multi-accelerometer based posture classifiers when dealing with natural movement and transitions and ii) maximize a wearable system's battery life through distributed computation at nodes. The EWV transition filters proposed here increase the classification accuracy by 1% over unfiltered results in realistic scenarios and significantly reduces spurious classifier output in real-time visualizations. A 200 fold transmission reduction from the on-body system to an outside system was achieved in practice by combining the transition filters with an event-based design. Furthermore, a method of reducing transmissions between on-body data gathering nodes based on distributed processing of the classifier rules (but maintaining a one-way flow of communications during system use) is also described. This provides a 3.3 fold reduction in packets and a 13.5 fold reduction in data transmitted from one node to the other in a two-node wearable system.

Original languageEnglish
Title of host publication2013 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)
PublisherIEEE
Pages635-640
Number of pages5
ISBN (Electronic)978-0-7695-5005-3
ISBN (Print)978-1-4799-0371-9
DOIs
Publication statusPublished - 16 Sep 2013
EventACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) - Central Michigan University, Mt Pleasant, United States
Duration: 1 Jul 20133 Jul 2013
Conference number: 14
http://www.ieee.org/conferences_events/conferences/conferencedetails/index.html?Conf_ID=30778

Conference

ConferenceACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)
CountryUnited States
CityMt Pleasant
Period1/07/133/07/13
Internet address

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Bibliographical note

Paper presented at the 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2013), held 1–3 July 2013, Honolulu Hawaii, U.S.A. This paper is due to be published by IEEE in the conference proceedings, and full citation details will be updated once available.
© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Keywords

  • body sensor networks
  • transition filters
  • distributed computation
  • Accuracy
  • Monitoring
  • Biomedical monitoring
  • Batteries
  • Real-time systems
  • Support vector machines
  • Decision trees
  • sensor fusion
  • data acquisition
  • data visualisation
  • information filtering
  • pattern classification
  • pose estimation
  • distributed processing
  • fielded autonomous posture classification system
  • realistic evaluation
  • body sensor network
  • multiaccelerometer based posture classifier rule
  • wearable system battery life maximization
  • EWV transition filter
  • classification accuracy
  • real-time visualization
  • on-body system
  • event-based design
  • on-body data gathering node

Cite this

Rednic, R., Kemp, J., Gaura, E., & Brusey, J. (2013). Fielded Autonomous Posture Classification Systems: Design and Realistic Evaluation. In 2013 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) (pp. 635-640). IEEE. https://doi.org/10.1109/SNPD.2013.114