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A computer-vision-based system for at-home rheumatoid arthritis rehabilitation

Author

Listed:
  • Javier Dorado
  • Xavier del Toro
  • Maria J Santofimia
  • Alfonso Parreño
  • Ruben Cantarero
  • Ana Rubio
  • Juan C Lopez

Abstract

People suffering from rheumatoid arthritis are normally prescribed with physical exercises. The realization of these exercise routines should be supervised to prevent wrongly adopted postures or bad movements that end up harming the affected articulations. The motivational component associated with a supervised session is also important. On the contrary, having access to qualified supervision is not always possible due to different reasons, such as tight schedules, limited mobility, or economic reasons. This article presents a system for at-home rehabilitation, based on Microsoft Kinect, that resembles the role played by the physiotherapist during the supervision of an exercise session. The system not only supervises the realization of exercises but it also collects and manages information about the exercise-session history. Additionally, and despite affecting young people, rheumatoid arthritis is a disease that mainly affects seniors. For this reason, the system provides a natural interface to ease the interaction. Finally, the performance of the proposed system has been evaluated using a three-dimensional dynamic measurement system as a gold standard to validate the accuracy of the measurements. Obtained results demonstrate the potential of the Kinect-based system for at-home rehabilitation exercise routines.

Suggested Citation

  • Javier Dorado & Xavier del Toro & Maria J Santofimia & Alfonso Parreño & Ruben Cantarero & Ana Rubio & Juan C Lopez, 2019. "A computer-vision-based system for at-home rheumatoid arthritis rehabilitation," International Journal of Distributed Sensor Networks, , vol. 15(9), pages 15501477198, September.
  • Handle: RePEc:sae:intdis:v:15:y:2019:i:9:p:1550147719875649
    DOI: 10.1177/1550147719875649
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    Cited by:

    1. Javier Dorado Chaparro & Jesús Fernández-Bermejo Ruiz & María José Santofimia Romero & Xavier del Toro García & Rubén Cantarero Navarro & Cristina Bolaños Peño & Henry Llumiguano Solano & Félix Jesús , 2022. "Phyx.io: Expert-Based Decision Making for the Selection of At-Home Rehabilitation Solutions for Active and Healthy Aging," IJERPH, MDPI, vol. 19(9), pages 1-20, May.

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