@article{ref1,
title="A machine learning framework for gait classification using inertial sensors: application to elderly, post-stroke and Huntington's disease patients",
journal="Sensors (Basel)",
year="2016",
author="Mannini, Andrea and Trojaniello, Diana and Cereatti, Andrea and Sabatini, Angelo Maria",
volume="16",
number="1",
pages="e16010134-e16010134",
abstract="Machine learning methods have been widely used for gait assessment through the estimation of spatio-temporal parameters. As a further step, the objective of this work is to propose and validate a general probabilistic modeling approach for the classification of different pathological gaits. Specifically, the presented methodology was tested on gait data recorded on two pathological populations (Huntington's disease and post-stroke subjects) and healthy elderly controls using data from inertial measurement units placed at shank and waist. By extracting features from group-specific Hidden Markov Models (HMMs) and signal information in time and frequency domain, a Support Vector Machines classifier (SVM) was designed and validated. The 90.5% of subjects was assigned to the right group after leave-one-subject-out cross validation and majority voting. The long-term goal we point to is the gait assessment in everyday life to early detect gait alterations.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s16010134",
url="http://dx.doi.org/10.3390/s16010134"
}