Page 408 - Control Theory in Biomedical Engineering
P. 408

Index  375


              Physiology                       R
               application, 12–15              Radial basis function (RBF), 106–107
               chaos, 15–18                    Range of motion (ROM), 236–237,
               control-based modeling, 4, 18–31, 29f  247–249, 256–257
               controllers, 4                  Rapid adaptors (RAs), 208
               control therapy, 29–31          Recursive least squares algorithm, 141
               dynamic behaviors, 15–17, 16f   Regression model, 133
               mathematical modeling, 3–18     RehabArm, 236–237
               modeling approaches, 5–9        Rehabilitation programs, ULD
               modeling methodology, 4–5, 5f     control approaches, 253–256
               positive loops, childbirth, 25–27, 27f  design requirements and challenges,
              Plasma insulin concentration (PIC),   247–253
                   64–65                         existingupperlimbexoskeletons,239–247
               bounds, 70–71                     recovery, upper limb dysfunction,
               cognizant adaptive-learning model    235–236
                   predictive control algorithm  robot-assisted (see Robot-assisted
                 adaptive glycemic and plasma insulin  rehabilitation programs)
                   risk indexes, 70            Rehabilitation robotics
                 adaptive-learning model predictive  classification, related devices, 182–190,
                   control formulation, 71–73       184–189f, 186–187t
                 feature extraction for manipulating  history, 182
                   constraints, 71               literature review, 181, 181f
                 plasma insulin concentration bounds,  motivations, 177–181
                   70–71                       Remnants. See Chylomicron remnants (CR)
              Plasma insulin risk index (PIRI), 70  Respiratory system, 7
              Plasma membrane system, 10–11    6-REXOS, 236–237, 248–251
              Polyvinyl alcohol (PVA), 327, 336–340  RGD-8705, 300, 301f
              Pontryagin’s Maximum Principle, 90–92,  RGD-8730, 300, 300f
                   95–98                       Right bundle branch block (RBBB), 107,
              Positive Poisson’s ratio (PPR), 322, 325  109, 110f, 123
              Power transmission mechanism, 250–251  Robot-assisted rehabilitation programs
              Practical identifiability, 12, 13f,34  end-effector-type devices, 235–236
              Predator-prey model, 13–14         exoskeleton type devices, 236–237
              Premature ventricular contraction (PVC),  hardware design and control approaches,
                   107, 109, 110f                   236–237, 256
              Probabilistic neural network (PNN),  Robot-assisted surgery
                   106–107                       applications, 168–174, 170f
              ProBot, 166–167                      da Vinci SP surgical system, 171–173,
              Proportional control, electromyography,  173f
                   215                             EndoWrist SP instruments, 171–173,
              Prosthetic devices, 159, 180–181      174f
                                                   2
              Prosthetic robots, 210               i Snake robot, 171, 171f
              Psoriasis treatment, 30              modular robot, 173, 175f
              Pulse-width modulation (PWM), 297    neuromate robot, 171, 172f
              PUMA robot, 166–167                FDA-approved devices and platforms,
              P wave, 109                           175–176, 176t, 177–180f
              Pythonflex, 327, 333               history, 166–168, 169f
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