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Sensors 2016, 16(1), 134; doi:10.3390/s16010134

A Machine Learning Framework for Gait Classification Using Inertial Sensors: Application to Elderly, Post-Stroke and Huntington’s Disease Patients

1
The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa 56127, Italy
2
Information Engineering Unit, POLCOMING Department, University of Sassari, Sassari 07100, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 7 December 2015 / Revised: 16 January 2016 / Accepted: 18 January 2016 / Published: 21 January 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [948 KB, uploaded 21 January 2016]   |  

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. View Full-Text
Keywords: gait classification; wearable sensors; inertial sensors; hidden Markov model; elderly; hemiparetic; Huntington’s disease gait classification; wearable sensors; inertial sensors; hidden Markov model; elderly; hemiparetic; Huntington’s disease
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Mannini, A.; Trojaniello, D.; Cereatti, A.; Sabatini, A.M. A Machine Learning Framework for Gait Classification Using Inertial Sensors: Application to Elderly, Post-Stroke and Huntington’s Disease Patients. Sensors 2016, 16, 134.

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