Gait control system for functional electrical stimulation using neural networks |
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Authors: | K Y Tong Dr M H Granat |
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Institution: | (1) Bioengineering Unit, University of Strathclyde, Wolfson Centre, 106 Rottenrow, G4 0NW Glasgow, UK |
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Abstract: | In functional electrical stimulation (FES) systems for restoring walking in spinal cord injured (SCI) individuals, hand switches
are the preferred method for controlling stimulation timing. Through practice the user becomes an ‘expert’ in determining
when stimulation should be applied. Neural networks have been used to ‘clone’ this expertise but these applications have used
small numbers of sensors, and their structure has used a binary output, giving rise to possible controller oscillations. It
was proposed that a threelayer structure neural network with continuous function, using a larger number of sensors, including
‘virtual’ sensors, can be used to ‘clone’ this expertise to produce good controllers. Using a sensor set of ten force sensors
and another of 13 ‘virtual’ kinematic sensors, a good FES control system was constructed using a three-layer neural network
with five hidden nodes. The sensor set comprising three sensors showed the best performance. The accuracy of the optimum three-sensor
set for the force sensors and the virtual kinematic sensors was 90% and 93%, respectively, compared with 81% and 77% for a
heel switch. With 32 synchronised sensors, binary neural networks and continuous neural networks were constructed and compared.
The networks using continuous function had significantly fewer oscillations. Continuous neural networks offer the ability
to generate good FES controllers. |
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Keywords: | Neural networks Virtual sensors Functional electrical stimulation Spinal cord injury |
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