Citation

Mathie MJ, Celler BG, Lovell NH, Coster AC. Med. Biol. Eng. Comput. 2004; 42(5): 679-687.

Affiliation

Centre for Health Informatics, University of New South Wales, Sydney, Australia.

Copyright

(Copyright © 2004, International Federation for Medical and Biological Engineering, Publisher Holtzbrinck Springer Nature Publishing Group)

DOI

unavailable

PMID

15503970

Abstract

A generic framework for the automated classification of human movements using an accelerometry monitoring system is introduced. The framework was structured around a binary decision tree in which movements were divided into classes and subclasses at different hierarchical levels. General distinctions between movements were applied in the top levels, and successively more detailed subclassifications were made in the lower levels of the tree. The structure was modular and flexible: parts of the tree could be reordered, pruned or extended, without the remainder of the tree being affected. This framework was used to develop a classifier to identify basic movements from the signals obtained from a single, waist-mounted triaxial accelerometer. The movements were first divided into activity and rest. The activities were classified as falls, walking, transition between postural orientations, or other movement. The postural orientations during rest were classified as sitting, standing or lying. In controlled laboratory studies in which 26 normal, healthy subjects carried out a set of basic movements, the sensitivity of every classification exceeded 87%, and the specificity exceeded 94%; the overall accuracy of the system, measured as the number of correct classifications across all levels of the hierarchy, was a sensitivity of 97.7% and a specificity of 98.7% over a data set of 1309 movements.