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HMM Based Evaluation of Physical Therapy Movements Using Kinect Tracking

  • Carlos PalmaEmail author
  • Augusto Salazar
  • Francisco Vargas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9474)

Abstract

Recognition of human activities in videos has experienced considerable changes with the introduction of cost-effective technology that allows for the tracking of individual body parts. This has led to the development of numerous tele-health applications that aim to help patients in their recovery process. Most of these systems are based on techniques to measure the degree of similarity of time series, together with thresholds to evaluate whether the movement satisfies the specification. This means that sequences similar enough to a template, but containing deviations from the correct form, may be considered correct, and thus the quality of movement incorrectly assessed. In this paper we propose the use of Hidden Markov Models as novelty detectors to evaluate the quality of movement in human beings. The results show the potential of this approach in detecting the sequences that deviate from normality for a wide range of activities common in physical therapy and rehabilitation.

Notes

Acknowledgment

This work was funded by Ruta N (Regalías de la Nación), número del convenio: 512C-2013. Código SUI (Viceinvestigaciones UdeA): 20139080.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Carlos Palma
    • 1
    Email author
  • Augusto Salazar
    • 1
    • 2
  • Francisco Vargas
    • 1
  1. 1.Grupo SISTEMIC, Facultad de IngenieríasUniversidad de Antioquia UdeAMedellínColombia
  2. 2.Grupo de Investigación AEyCC, Facultad de IngenieríasInstituto Tecnológico Metropolitano ITMMedellínColombia

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