Page 344 - Handbook of Biomechatronics
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Upper Extremity Rehabilitation Robots: A Survey              337


                 In the human body, the arm motion is controlled by the CNS, so control-
              lers that have any characteristics of the CNS might be advantageous for reha-
              bilitation robotics. Since triggered passive controllers are dealing with
              biosignals, they can provide powerful tools for rehabilitation by inclusion
              of biological feedback. Thus, recent developments in rehabilitation robotics
              aremostlyfocusedonleveragingthesetypeofcontrollerstoimprovethequal-
              ity of biologically plausible therapy. Furthermore, model-based controllers
              (e.g.,NMPC)canalsoprovidebiomechanicallyplausibletools forrehabilitation;
              consequently, some studies in recent years have been focused on this idea.
                 Traditional physical therapies suffer from various inadequacies
              (Jorgensen et al., 1995; Ifejika-Jones and Barrett, 2011) and may result in
              significant financial burdens from costly therapy sessions (Dong et al.,
              2006; Krebs and Hogan, 2012). It is important to continue advancing reha-
              bilitation robots, supported by innovative motor learning scenarios (Brewer
              et al., 2007; Cano-de-la Cuerda et al., 2015) and the optimization of
              mechatronic design and control algorithms, since they can result in effective
              in-home rehabilitation and patient care (Dong et al., 2006; Poli et al., 2013).
              Furthermore, these interactive and friendly robots can provide variations in
              delivering therapy (building on new achievements in motor learning studies)
              (Brewer et al., 2007; Reinkensmeyer, 2009), and meaningful restoration of
              functional activities (Krebs and Volpe, 2013). In conclusion, we fully expect
              that more progress will be made in the near future to improve the design
              and control of rehabilitation robots for providing biologically plausible
              autonomous therapy.



                   GLOSSARY


                     Artificial intelligence
              AI
                     Brain-computer interface
              BCI
                     Central nervous system
              CNS
                     Degree-of-freedom
              DOF
                     Electroencephalogram
              EEG
                     Electromyography
              EMG
                     Functional electrical stimulation
              FES
                     Functional magnetic resonance imaging
              fMRI
                     Functional near-infrared spectroscopy
              fNIRS
                     Human-robot interaction
              HRI
                     Model-based system engineering
              MBSE
                     Nonlinear model predictive control
              NMPC
                     Range of motion
              ROM
                     Toronto Rehabilitation Institute
              TRI
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