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  • Research Article
  • Open Access

Detection of Early Morning Daily Activities with Static Home and Wearable Wireless Sensors

  • 1, 2,
  • 1,
  • 1Email author and
  • 1
EURASIP Journal on Advances in Signal Processing20072008:273130

https://doi.org/10.1155/2008/273130

  • Received: 1 March 2007
  • Accepted: 12 July 2007
  • Published:

Abstract

This paper describes a flexible, cost-effective, wireless in-home activity monitoring system for assisting patients with cognitive impairments due to traumatic brain injury (TBI). The system locates the subject with fixed home sensors and classifies early morning bathroom activities of daily living with a wearable wireless accelerometer. The system extracts time- and frequency-domain features from the accelerometer data and classifies these features with a hybrid classifier that combines Gaussian mixture models and a finite state machine. In particular, the paper establishes that despite similarities between early morning bathroom activities of daily living, it is possible to detect and classify these activities with high accuracy. It also discusses system training and provides data to show that with proper feature selection, accurate detection and classification are possible for any subject with no subject specific training.

Keywords

  • Traumatic Brain Injury
  • Feature Selection
  • Daily Living
  • Mixture Model
  • State Machine

Publisher note

To access the full article, please see PDF.

Authors’ Affiliations

(1)
Department of Electrical and Computer Engineering, University of Minnesota, MN 55455, USA
(2)
Department of Veterans Affairs, Minneapolis VA Medical Center, Minnesota, MN 55417, USA

Copyright

© Nuri Firat Ince et al. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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