Evaluation of Self-Rehabilitation Movements by Hidden Markov Model

Yves Rybarczyk, Jan Kleine Deters

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

This study aims to propose a statistical model to automatically assess the correctness of rehabilitation movements performed by patients. Ten Hidden Markov Models are developed and trained, in order to discriminate in real time the main faults in the execution of therapeutic exercises for reeducation after hip replacement surgery. An experiment on real patients shows that the algorithm is as accurate as the physiotherapists to discriminate and identify the error in the movement. The results are discussed in terms of the required setup for a successful implementation of this method in a tele-rehabilitation platform.

Original languageEnglish
Title of host publication2018 IEEE (SMC) International Conference on Innovations in Intelligent Systems and Applications, INISTA 2018
EditorsPlamen Angelov, Tulay Yildirim, Lazaros Iliadis, Yannis Manolopoulos
PublisherIEEE
ISBN (Electronic)9781538651506
DOIs
Publication statusPublished - 14 Sep 2018
Event2018 IEEE International Conference on Innovations in Intelligent Systems and Applications, INISTA 2018 - Thessaloniki, Greece
Duration: 3 Jul 20185 Jul 2018

Conference

Conference2018 IEEE International Conference on Innovations in Intelligent Systems and Applications, INISTA 2018
Abbreviated titleINISTA 2018
CountryGreece
CityThessaloniki
Period3/07/185/07/18

Keywords

  • decision support systems
  • health computing
  • machine learning
  • movement recognition
  • probabilistic model

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