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.

A machine learning framework for gait classification using inertial sensors: Application to elderly, post-stroke and huntington’s disease patients / Mannini, Andrea; Trojaniello, Diana; Cereatti, Andrea; Sabatini, Angelo M.. - In: SENSORS. - ISSN 1424-8220. - 16:1(2016), p. 134. [10.3390/s16010134]

A machine learning framework for gait classification using inertial sensors: Application to elderly, post-stroke and huntington’s disease patients

TROJANIELLO, Diana;CEREATTI, Andrea;
2016-01-01

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.
2016
A machine learning framework for gait classification using inertial sensors: Application to elderly, post-stroke and huntington’s disease patients / Mannini, Andrea; Trojaniello, Diana; Cereatti, Andrea; Sabatini, Angelo M.. - In: SENSORS. - ISSN 1424-8220. - 16:1(2016), p. 134. [10.3390/s16010134]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11388/163218
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