Open Science Research Excellence
%0 Journal Article
%A Alexandros Lioulemes and  Michail Theofanidis and  Varun Kanal and  Konstantinos Tsiakas and  Maher Abujelala and  Chris Collander and  William B. Townsend and  Angie Boisselle and  Fillia Makedon
%D 2017 
%J  International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering
%B World Academy of Science, Engineering and Technology
%I International Science Index 124, 2017
%T MAGNI Dynamics: A Vision-Based Kinematic and Dynamic Upper-Limb Model for Intelligent Robotic Rehabilitation
%U http://waset.org/publications/10006973
%V 124
%X This paper presents a home-based robot-rehabilitation
instrument, called ”MAGNI Dynamics”, that utilized a vision-based
kinematic/dynamic module and an adaptive haptic feedback
controller. The system is expected to provide personalized
rehabilitation by adjusting its resistive and supportive behavior
according to a fuzzy intelligence controller that acts as an inference
system, which correlates the user’s performance to different stiffness
factors. The vision module uses the Kinect’s skeletal tracking to
monitor the user’s effort in an unobtrusive and safe way, by estimating
the torque that affects the user’s arm. The system’s torque estimations
are justified by capturing electromyographic data from primitive
hand motions (Shoulder Abduction and Shoulder Forward Flexion).
Moreover, we present and analyze how the Barrett WAM generates
a force-field with a haptic controller to support or challenge the
users. Experiments show that by shifting the proportional value,
that corresponds to different stiffness factors of the haptic path, can
potentially help the user to improve his/her motor skills. Finally,
potential areas for future research are discussed, that address how
a rehabilitation robotic framework may include multisensing data, to
improve the user’s recovery process.
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