title: A Machine Learning Framework for Gait Classification Using Inertial Sensors: Application to Elderly, Post-Stroke and Huntington's Disease Patients.
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.
authors.affiliation: The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa 56127, Italy. a.mannini@sssup.it.
authors.forename: Andrea
authors.initials: A
authors.lastname: Mannini
authors.affiliation: Information Engineering Unit, POLCOMING Department, University of Sassari, Sassari 07100, Italy. dtrojaniello@uniss.it.
authors.forename: Diana
authors.initials: D
authors.lastname: Trojaniello
authors.affiliation: Information Engineering Unit, POLCOMING Department, University of Sassari, Sassari 07100, Italy. acereatti@uniss.it.
authors.forename: Andrea
authors.initials: A
authors.lastname: Cereatti
authors.affiliation: The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa 56127, Italy. sabatini@sssup.it.
authors.forename: Angelo M
authors.initials: AM
authors.lastname: Sabatini
mesh_terms: D061725:Accelerometry; D000368:Aged; D005260:Female; D005684:Gait / Q000502:physiology*; D006801:Humans; D006816:Huntington Disease / Q000503:physiopathology*; D000069550:Machine Learning; D008297:Male; D008390:Markov Chains; D008875:Middle Aged; D018670:Monitoring, Ambulatory; D010291:Paresis / Q000503:physiopathology; D012815:Signal Processing, Computer-Assisted; D020521:Stroke / Q000503:physiopathology*; D060388:Support Vector Machine
keywords: Huntington’s disease; elderly; gait classification; hemiparetic; hidden Markov model; inertial sensors; wearable sensors