Journal
SENSORS
Volume 16, Issue 1, Pages -Publisher
MDPI
DOI: 10.3390/s16010134
Keywords
gait classification; wearable sensors; inertial sensors; hidden Markov model; elderly; hemiparetic; Huntington's disease
Funding
- Italian Ministry of Education and Research (MIUR) through project (PRIN) A quantitative and multi-factorial approach for estimating and preventing the risk of falls in the elderly people
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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.
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