Introduction Parkinson’s, stroke, and other neurological diseases may significantly affect the control of voluntary, ballistic-like movements that normally are performed automatically and optimally as regards position accuracy, energy expenditure and movement execution time. The control functi...
Introduction Parkinson’s, stroke, and other neurological diseases may significantly affect the control of voluntary, ballistic-like movements that normally are performed automatically and optimally as regards position accuracy, energy expenditure and movement execution time. The control functions (neural signals to muscles) are to be re-learnt and re-optimised with respect to these performance indices. In our study, a natural approach for efficient motor learning in goal-directed motion tasks, incl. walking is proposed. It is based on novel concepts and underlying principles of neurophysiology, natural dynamics, and biological cybernetics. We believe that such a motor control and learning strategy may be very useful to achieve efficiency in neurorehabilitation. Method Test control functions that could sensibly optimize motor performance have a triphasic (burst-pause-burst) shape. Their parameters are intrinsic ones that human has to (re-) learn in dynamic point-to-point motion tasks, (). The control learning scheme has the following main steps, (): (1) parameterise test control functions; (2) select most appropriate pairs of control parameters and controlled outputs; (3) make corrections in the control parameters until reach the target, applying simple, natural learning algorithm, within a very low number of trials. Results Using realistic mathematical models, our motor learning approach was applied to motion tasks like reaching movements, , and performing steps. In the latter case, we decomposed the task to perform a step into two point-to-point leg movements, . In the computer simulations we verified our control concepts and the fact that the learning control process is fast converging. In addition, we did some real (able-bodied) experiments with rapid aiming movements of the arm and they confirm the feasibility and efficacy of the proposed approach. Discussion Based on the conceptual framework above proposed it is possible to design reliable control strategies in neurorehabilitation, (). The neural structures that compute the required control forces are the so-called internal models, and we believe that the proposed approach can be used to rebuild such models (cortical reorganization) by proper training procedures. Recent research has shown that voluntary exercises can increase levels of brain-derived neurotrophic factor and other growth factors, stimulate neurogenesis, and improve brain plasticity and motor learning performance. Conclusion Our approach can also be used for the purposes of neuro-muscular rehabilitation, with assistive robotic devices applied or in functional electrical stimulation. In the latter case, optimising the timing sequence for stimulating muscles may produce smoother and more accurate movements. Another interesting problem is the design of brain-computer interfaces for direct control of human/robot motion and this is a subject of ongoing research.
Introduction Parkinson’s, stroke, and other neurological diseases may significantly affect the control of voluntary, ballistic-like movements that normally are performed automatically and optimally as regards position accuracy, energy expenditure and movement execution time. The control functions (neural signals to muscles) are to be re-learnt and re-optimised with respect to these performance indices. In our study, a natural approach for efficient motor learning in goal-directed motion tasks, incl. walking is proposed. It is based on novel concepts and underlying principles of neurophysiology, natural dynamics, and biological cybernetics. We believe that such a motor control and learning strategy may be very useful to achieve efficiency in neurorehabilitation. Method Test control functions that could sensibly optimize motor performance have a triphasic (burst-pause-burst) shape. Their parameters are intrinsic ones that human has to (re-) learn in dynamic point-to-point motion tasks, (). The control learning scheme has the following main steps, (): (1) parameterise test control functions; (2) select most appropriate pairs of control parameters and controlled outputs; (3) make corrections in the control parameters until reach the target, applying simple, natural learning algorithm, within a very low number of trials. Results Using realistic mathematical models, our motor learning approach was applied to motion tasks like reaching movements, , and performing steps. In the latter case, we decomposed the task to perform a step into two point-to-point leg movements, . In the computer simulations we verified our control concepts and the fact that the learning control process is fast converging. In addition, we did some real (able-bodied) experiments with rapid aiming movements of the arm and they confirm the feasibility and efficacy of the proposed approach. Discussion Based on the conceptual framework above proposed it is possible to design reliable control strategies in neurorehabilitation, (). The neural structures that compute the required control forces are the so-called internal models, and we believe that the proposed approach can be used to rebuild such models (cortical reorganization) by proper training procedures. Recent research has shown that voluntary exercises can increase levels of brain-derived neurotrophic factor and other growth factors, stimulate neurogenesis, and improve brain plasticity and motor learning performance. Conclusion Our approach can also be used for the purposes of neuro-muscular rehabilitation, with assistive robotic devices applied or in functional electrical stimulation. In the latter case, optimising the timing sequence for stimulating muscles may produce smoother and more accurate movements. Another interesting problem is the design of brain-computer interfaces for direct control of human/robot motion and this is a subject of ongoing research.
참고문헌 (3)
Biol Cybern Karniel 77 3 173 1997 10.1007/s004220050378 A model for learning human reaching movements
Kiryazov 1219 2010 Emerging trends in mobile robotics Efficient learning approach for optimal control of human and robot motion
Despotova D, Kiriazov P. Motor learning and optimisation in gait control. In: Proc. of the 5th European conference, technically assisted rehabilitation, Berlin; 2015.
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