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.
|Titolo:||A machine learning framework for gait classification using inertial sensors: Application to elderly, post-stroke and huntington’s disease patients|
|Data di pubblicazione:||2016|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.3390/s16010134|
|Appare nelle tipologie:||1.1 Articolo in rivista|