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Bag-of-steps: Predicting Lower-limb Fracture Rehabilitation Length

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

Neurocomputing, 2016, In Press

Elsevier

Autor: Pla, Albert
Mordvanyuk, Natalia
López Ibáñez, Beatriz
Raaben, Marco
Blokhuid, Taco J.
Holstlag, Herman R.
Data: 4 maig 2017
Resum: 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
Format: application/pdf
Cita: https://doi.org/10.1016/j.neucom.2016.11.084
ISSN: 0925-2312
Accés al document: http://hdl.handle.net/10256/14063
Llenguatge: eng
Editor: Elsevier
Col·lecció: Versió postprint del document publicat a: https://doi.org/10.1016/j.neucom.2016.11.084
Articles publicats (D-EEEiA)
És part de: Neurocomputing, 2016, In Press
Drets: Tots els drets reservats
Matèria: Medicina -- Informàtica
Medicine -- Data processing
Títol: Bag-of-steps: Predicting Lower-limb Fracture Rehabilitation Length
Tipus: info:eu-repo/semantics/article
Repositori: DUGiDocs

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