The overall objective is to develop a new strategy for motor control of
functional hand prostheses based on electrical signals generated from
multiple muscle electrodes or microchips implanted in the peripheral or
central nervous system. The use of Artificial Neural Network (ANN) is
essential to fulfil this purpose.
The purpose is also to develop systems
for artificial sensibility to be applied to such hand prostheses and to
patients with loss of sensory nerve function. The overall goal is to create
new possibilities for rehabilitation of amputees and paralysed patients.
The project is multidisciplinary and involves several subprojects. We have
so far been able to demonstrate that rat sciatic axons are capable of
regenerating through the via holes of an implanted silicon sieve electrode.
Furthermore we can register nerve signals via the chip after electrical
stimulation of the nerve roots. An in vitro model has been set up and used
to demonstrate that certain chip design can reduce the problem of
crosstalk.
In future experiments the influence of chip design on
regeneration success will be determined. We have also demonstrated that
central nervous axons are capable of growing into a chip if attracted by
pieces of peripheral nerve. ANN has been used to recognise complex muscle
signals from multiple surface electrodes in order to associate specific
signal patterns with specific movement of a virtual hand.
In two
experiments the principle of artificial sensibility has been tested by
either using piezoresistive sensors or sensors based on detecting
vibrotactile stimuli using hearing sense. These experiments indicate that
it is possible to create artificial sensibility in a prosthesis or a hand
with sensory dysfunction.
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