Bag-of-steps: Predicting Lower-limb Fracture Rehabilitation Length
dc.contributor.author
dc.date.accessioned
2017-05-25T10:18:10Z
dc.date.available
2021-04-07T08:03:00Z
dc.date.issued
2017-12-13
dc.identifier.issn
0925-2312
dc.identifier.uri
dc.description.abstract
Lower-limb fracture surgery is one of the major causes for autonomy loss among aged people. For care institutions, tackling with an optimized rehabilitation process is a key factor as it improves both the patients quality of life and the associated costs of the after surgery process. This paper presents bag-of-steps, a new methodology to predict the rehabilitation length and discharge date of a patient using insole force sensors and a predictive model based on the bag-of-words technique. The sensors information is used to characterize the patients gait creating a set of step descriptors. This descriptors are later used to define a vocabulary of steps using a clustering method. The vocabulary is used to describe rehabilitation sessions which are finally entered to a classifier that performs the final rehabilitation estimation. The methodology has been tested using real data from patients that underwent surgery after a lower-limb fracture
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
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Versió postprint del document publicat a: https://doi.org/10.1016/j.neucom.2016.11.084
dc.relation.ispartof
Neurocomputing, 2016, In Press
dc.relation.ispartofseries
Articles publicats (D-EEEiA)
dc.rights
Tots els drets reservats
dc.source
Pla, Albert Mordvanyuk, Natalia López Ibáñez, Beatriz Raaben, Marco Blokhuid, Taco J. Holstlag, Herman R. 2016 Bag-of-steps: Predicting Lower-limb Fracture Rehabilitation Length Neurocomputing
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dc.title
Bag-of-steps: Predicting Lower-limb Fracture Rehabilitation Length
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.embargo.terms
2019-12-13
dc.date.embargoEndDate
info:eu-repo/date/embargoEnd/2019-12-13
dc.type.version
info:eu-repo/semantics/acceptedVersion
dc.identifier.doi
dc.identifier.idgrec
026706