"Robotic haptic guidance is a motor-training strategy in which a machine physically interacts with the participant’s limbs during movement training. This strategy is commonly used to reduce performance errors while training dangerous tasks as relearning to walk after a neurologic injury. There is currently little evidence that robotic guidance is beneficial for human motor learning. In fact, a long-standing hypothesis states that physically guiding a movement impairs motor learning. However, recent studies have suggested that haptic guidance seems to be particularly helpful in less skilled participants, in the demonstration of optimal timing rather than movement magnitude. We propose a 2-year project that aims to identify which form of guidance -and for which patients' specific motor deficits -will optimize motor learning using the gait robotic rehabilitator Lokomat. We will identify the effect of different forms of guidance and resistance training on motor recovery of a timing task as walking. We will engineer a technique to quantify the individual disability level in order to provide each individual with the amount of guidance that would optimize learning, enhancing the beneficial use of the Lokomat robot for training less disable patients by means of more sophisticate control strategies, such as error amplification. The goal of robotic therapy is the development of robotic devices to perform exercises which provoke motor plasticity. However, it is still an open question how different rehabilitation strategies contribute to brain restorative processes. The results from studying the particular brain regions involved in learning might help tailoring motor training conditions to the anatomical location of a focal brain insult. To achieve this goal, we will evaluate the brain regions involved in learning when training with different forms of guidance, performing functional Magnetic Resonance Imaging (fMRI) while training with an fMRI compatible walking robotic device."
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