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MAGNI: A Real-Time Robot-Aided Game-Based Tele-Rehabilitation System

  • Srujana GattupalliEmail author
  • Alexandros Lioulemes
  • Shawn N. Gieser
  • Paul Sassaman
  • Vassilis Athitsos
  • Fillia Makedon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9739)

Abstract

During the last two decades, robotic rehabilitation has become widespread, particularly for upper limb physical rehabilitation. Major findings prove that the efficacy of robot-assisted rehabilitation can be increased by motivation and engagement, which is offered by exploiting the opportunities of gamification and exergaming. This paper presents a tele-rehabilitation framework to enable interaction between therapists and patients and is a combination of a graphical user interface and a high dexterous robotic arm. The system, called MAGNI, integrates a 3D exercise game with a robotic arm, operated by therapist in order to assign in real-time the prerecorded exercises to the patients. We propose a game that can be played by a patient who has suffered an injury to their arm (e.g. Stroke, Spinal Injury, or some physical injury to the shoulder itself). The experimental results and the feedback from the participants show that the system has the potential to impact how robotic physical therapy addresses specific patient’s needs and how occupational therapists assess patient’s progress over time.

Keywords

HCI Upper-limb rehabilitation Gamification 

Notes

Acknowledgment

This work is supported in part by the National Science Foundation under award numbers NSF-1338118 and NSF-1055062. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. The authors would like to thank Dr. Fillia Makedon who taught the Special Topics in HCI Course, in which this project was a part of.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Srujana Gattupalli
    • 1
    Email author
  • Alexandros Lioulemes
    • 2
  • Shawn N. Gieser
    • 2
  • Paul Sassaman
    • 2
  • Vassilis Athitsos
    • 1
  • Fillia Makedon
    • 2
  1. 1.VLM - The Vision-Learning-Mining Research Laboratory, Department of Computer Science and EngineeringUniversity of Texas at ArlingtonArlingtonUSA
  2. 2.HERACLEIA - Human-Center Computing Laboratory, Department of Computer Science and EngineeringUniversity of Texas at ArlingtonArlingtonUSA

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