Functional electrical stimulation involves artificial activation of paralyzed muscles with implanted electrodes and has been used successfully to improve the ability of quadriplegics to perform movements important for daily activities. The range of motor behaviors that can be generated by functional electrical stimulation, however, is limited to a relatively small set of preprogrammed movements such as hand grasp and release. A broader range of movements has not been implemented because of the substantial challenge associated with identifying the patterns of muscle stimulation needed to elicit specified movements. We plan to use a probabilistic algorithm to predict the patterns of muscle activity associated with a wide range of upper limb movements based on hand trajectory information. The predicted patterns of muscle activity will then be transformed into amplitude-modulated trains of pulses and used to drive muscle stimulators in order to evoke movements in temporarily paralyzed animals. The evoked movements are quantitatively compared to the desired movements to evaluate the overall effectiveness of this approach. Ultimately, this probabilistic method could serve as the requisite interface between brain-derived trajectory information and existing functional electrical stimulation systems to realize a self-contained and self-controlled upper limb neuroprosthetic system. Such an integrated and flexible system would greatly increase movement capability, and independence, in paralyzed individuals.
The goal of this project is to develop a new method to artificially activate and control paralyzed muscles with electrodes implanted in muscles. This effort will contribute to the restoration of voluntary limb movements in individuals paralyzed because of spinal cord injury or stroke.
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| Johnson, Lise A; Fuglevand, Andrew J (2009) Evaluation of probabilistic methods to predict muscle activity: implications for neuroprosthetics. J Neural Eng 6:055008 |
