Detection of Early Morning Daily Activities with Static Home and Wearable Wireless Sensors. (21st October 2007)

• Record Type: Journal Article
• Authors: Ince Ince, Nuri Firat Nuri Firat; Min Min, Cheol-Hong Cheol-Hong; Tewfik Tewfik, Ahmed Ahmed; Vanderpool Vanderpool, David David
• Other Names: Cetin Cetin Enis Ahmet Enis Ahmet Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2008(2008)
• Publication Date: 2007-10-21
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2008/273130
• Languages: English
• ISSNs: 1687-6172

Abstract

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.