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Open Access

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

  • Nuri Firat Ince1, 2,
  • Cheol-Hong Min1,
  • Ahmed Tewfik1Email author and
  • David Vanderpool1
EURASIP Journal on Advances in Signal Processing20072008:273130

Received: 1 March 2007

Accepted: 12 July 2007

Published: 17 July 2007


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.


Traumatic Brain InjuryFeature SelectionDaily LivingMixture ModelState Machine

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Authors’ Affiliations

Department of Electrical and Computer Engineering, University of Minnesota, USA
Department of Veterans Affairs, Minneapolis VA Medical Center, Minnesota, USA


© 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.