Abstract
Clinical studies have so far not been able to show if robotic therapy is superior to conventional methods. The personalization of robot-assisted therapy according to the individual motor deficits might contribute to reach this goal. Here we present a statistical approach to automatically personalize robotic rehabilitation. Our method uses different motor performance measures to estimate motor improvement and adapt the motor task within a treatment session. This approach was tested with a pilot sub-acute stroke patient and the outcome was compared to a similar patient who underwent conventional physical therapy. Pilot results showed better outcomes in clinical tests, kinematics and muscle activity for the subject who trained using the personalized robotic approach.
Research supported by Wyss Center for Bio- and Neuro- Engineering and Bertarelli Foundation Chair in Translational Neuroengineering.
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Pierella, C. et al. (2019). Personalizing Exoskeleton-Based Upper Limb Rehabilitation Using a Statistical Model: A Pilot Study. In: Masia, L., Micera, S., Akay, M., Pons, J. (eds) Converging Clinical and Engineering Research on Neurorehabilitation III. ICNR 2018. Biosystems & Biorobotics, vol 21. Springer, Cham. https://doi.org/10.1007/978-3-030-01845-0_23
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DOI: https://doi.org/10.1007/978-3-030-01845-0_23
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