Detection of early morning daily activities with static home and wearable wireless sensors

Authors:
Nuri Firat Ince;Cheol-Hong Min;Ahmed Tewfik;David Vanderpool
Affiliations:
Department of Electrical and Computer Engineering, University of Minnesota, MN and Minneapolis VA Medical Center, Department of Veterans Affairs, Minnesota, MN;Department of Electrical and Computer Engineering, University of Minnesota, MN;Department of Electrical and Computer Engineering, University of Minnesota, MN;Department of Electrical and Computer Engineering, University of Minnesota, MN
Venue:
EURASIP Journal on Advances in Signal Processing
Year:
2008

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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.