The effect of gamified robot-enhanced training on motor performance in chronic stroke survivors
Arzu Guneysu Ozgur,
Maximilian J. Wessel,
Jennifer K. Olsen,
Andéol Geoffroy Cadic-Melchior,
Valérie Zufferey,
Wafa Johal,
Giulia Dominijanni,
Jean-Luc Turlan,
Andreas Mühl,
Barbara Bruno,
Philippe Vuadens,
Pierre Dillenbourg,
Friedhelm C. Hummel
Affiliations
Arzu Guneysu Ozgur
Computer Human Interaction in Learning and Instruction (CHILI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Division of Robotics, Perception and Learning (RPL), EECS, KTH Royal Institute of Technology, Stockholm, Sweden; Corresponding author.
Maximilian J. Wessel
Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Clinique Romande de Réadaptation, Neuro-X Institute (INX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Sion, Switzerland; Department of Neurology, University Hospital and Julius-Maximilians-University, Wuerzburg, Germany
Jennifer K. Olsen
University of San Diego, San Diego, CA, USA
Andéol Geoffroy Cadic-Melchior
Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Clinique Romande de Réadaptation, Neuro-X Institute (INX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Sion, Switzerland
Valérie Zufferey
Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Clinique Romande de Réadaptation, Neuro-X Institute (INX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Sion, Switzerland
Wafa Johal
School of Computing and Information Systems, University of Melbourne, Victoria, Australia
Giulia Dominijanni
Bertarelli Foundation Chair in Translational NeuroEngineering, Center for Neuroprosthetics and School of Engineering, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
Jean-Luc Turlan
Neurological Rehabilitation Department of Clinique Romande de Réadaptation (SUVA), Sion, Switzerland
Andreas Mühl
Neurological Rehabilitation Department of Clinique Romande de Réadaptation (SUVA), Sion, Switzerland
Barbara Bruno
Computer Human Interaction in Learning and Instruction (CHILI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
Philippe Vuadens
Neurological Rehabilitation Department of Clinique Romande de Réadaptation (SUVA), Sion, Switzerland
Pierre Dillenbourg
Computer Human Interaction in Learning and Instruction (CHILI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
Friedhelm C. Hummel
Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (INX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland; Defitech Chair of Clinical Neuroengineering, Clinique Romande de Réadaptation, Neuro-X Institute (INX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Sion, Switzerland; Clinical Neuroscience, University of Geneva Medical School, Geneva, Switzerland
Task-specific training constitutes a core element for evidence-based rehabilitation strategies targeted at improving upper extremity activity after stroke. Its combination with additional treatment strategies and neurotechnology-based solutions could further improve patients' outcomes. Here, we studied the effect of gamified robot-assisted upper limb motor training on motor performance, skill learning, and transfer with respect to a non-gamified control condition with a group of chronic stroke survivors. The results suggest that a gamified training strategy results in more controlled motor performance during the training phase, which is characterized by a higher accuracy (lower deviance), higher smoothness (lower jerk), but slower speed. The responder analyses indicated that mildly impaired patients benefited most from the gamification approach. In conclusion, gamified robot-assisted motor training, which is personalized to the individual capabilities of a patient, constitutes a promising investigational strategy for further improving motor performance after a stroke.
