Bag-of-steps: Predicting lower-limb fracture rehabilitation length by weight loading analysis

Albert Pla*, Natalia Mordvanyuk, Beatriz Lopez, Marco Raaben, Taco J. Blokhuis, Herman R. Holstlag

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

5 Citations (Web of Science)

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. (C) 2017 Elsevier B.V. All rights reserved.

Original languageEnglish
Pages (from-to)109-115
Number of pages7
JournalNeurocomputing
Volume268
DOIs
Publication statusPublished - 13 Dec 2017

Keywords

  • Medical informatics
  • Gait analysis
  • Bag-of-words
  • Support vector machines
  • Clustering
  • Pattern recognition
  • Health
  • ARTIFICIAL NEURAL-NETWORKS
  • SUPPORT VECTOR MACHINES
  • GAIT EVENT DETECTION
  • HIP FRACTURE
  • CLASSIFICATION

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