Abstract:
:In this paper, a Hidden Semi-Markov Model (HSMM) based approach is proposed to evaluate and monitor body motion during a rehabilitation training program. The approach extracts clinically relevant motion features from skeleton joint trajectories, acquired by the RGB-D camera, and provides a score for the subject's performance. The approach combines different aspects of rule and template based methods. The features have been defined by clinicians as exercise descriptors and are then assessed by a HSMM, trained upon an exemplar motion sequence. The reliability of the proposed approach is studied by evaluating its correlation with both a clinical assessment and a Dynamic Time Warping (DTW) algorithm, while healthy and neurological disabled people performed physical exercises. With respect to the discrimination between healthy and pathological conditions, the HSMM based method correlates better with the physician's score than DTW. The study supports the use of HSMMs to assess motor performance providing a quantitative feedback to physiotherapist and patients. This result is particularly appropriate and useful for a remote assessment in the home.
journal_name
J Biomed Informjournal_title
Journal of biomedical informaticsauthors
Capecci M,Ceravolo MG,Ferracuti F,Iarlori S,Kyrki V,Monteriù A,Romeo L,Verdini Fdoi
10.1016/j.jbi.2017.12.012subject
Has Abstractpub_date
2018-02-01 00:00:00pages
1-11eissn
1532-0464issn
1532-0480pii
S1532-0464(17)30282-4journal_volume
78pub_type
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