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

NEUROCOMPUTING

卷:268

Article

Pla, Albert^1,2  Mordvanyuk, Natalia²  Lopez, Beatriz²  Raaben, Marco³  Blokhuis, Taco J.⁴  Holstlag, Herman R.⁵ 

[1] Univ Oslo, Dept Med Genet, N-0313 Oslo, Norway

[2] Univ Girona, Dept Elect Engn Elect & Automat, Girona 17003, Spain

[3] Univ Med Ctr Utrecht, NL-3508 GA Utrecht, Netherlands

[4] Maastricht Univ, Med Ctr, Dept Surg, NL-6202 AZ Maastricht, Netherlands

[5] Acad Med Ctr, Dept Rehabil Med, NL-1105 AZ Amsterdam, Netherlands

关键词: Medical informatics;    Gait analysis;    Bag-of-words;    Support vector machines;    Clustering;    Pattern recognition;    Health;   

DOI  :  10.1016/j.neucom.2016.11.084

来源: Elsevier

【 摘 要 】

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.

【 授权许可】

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