NDLI: Classification of continuously executed early morning activities using wearable wireless sensors

Content Provider	IEEE Xplore Digital Library
Author	Min, Cheol-Hong Ince, Nuri F. Tewfik, Ahmed H.
Copyright Year	2008
Description	Author affiliation: Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, 55455 USA (Min, Cheol-Hong; Ince, Nuri F.; Tewfik, Ahmed H.)
Abstract	In this paper, we study the personal monitoring system that classifies the continuously executed early morning activities of daily living. The system is intended to assist those with cognitive impairments due to traumatic brain injuries. The system can be used to help therapists in hospitals or could be deployed in one's home to track and monitor the activities executed by the recovering patients. We begin by briefly describing the infrastructure of our cost-effective system which uses fixed and wearable wireless sensors and show results related to the detection of activities continuously executed in the morning. Both frequency and time domain features from an accelerometer attached to the right wrist were extracted and used for classification using Gaussian mixture models, followed by a finite state machine. We show promising classification results obtained from 5 subjects. Overall classification rate is 88.3 % for 4 activities of interests.
Starting Page	5192
Ending Page	5195
File Size	1343747
Page Count	4
File Format	PDF
ISBN	9781424418145
ISSN	1557170X
DOI	10.1109/IEMBS.2008.4650384
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-20
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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