@inproceedings{45084a115c244fe8b44dedeebbecc2f1,
title = "Gait Patterns Classification using Spectral Features",
abstract = "Accelerometry has been shown to be a good tool for ambulatory activity monitoring. This paper describes the use of spectral features for classification of gait activities based on accelerometric data. The classification is performed by a Gaussian mixture model (GMM) based statistical classifier at the back end. Fifty subjects participated in the experiment and an overall classification accuracy of 86\% was achieved using the proposed 25 dimensional features for five different human gait patterns including walking on level surfaces, walking up and down stairs and walking up and down ramps.",
keywords = "Accelerometry, Ambulatory monitoring, Feature extraction, Gait patterns",
author = "Liam Kilmartin",
year = "2008",
month = jun,
day = "1",
language = "English (Ireland)",
isbn = "9780863419317",
series = "IET Conference Publications",
pages = "98--102",
booktitle = "Irish Signals and Systems Conference 2008",
note = "IET Irish Signals and Systems Conference, ISSC 2008 ; Conference date: 18-06-2008 Through 19-06-2008",
}