Luigi Borda
AI-based optimization of somatosensory neuroprosthetic stimulation through learning control algorithms.
Rel. Danilo Demarchi. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2021
Abstract
Millions of people worldwide suffer from peripheral neuropathy, which damages the peripheral nervous system with a devastating impact on the quality of life. Peripheral neuropathy causes reduced peripheral sensitivity of the affected limbs and hence impacts the control of proprioceptive feedback during locomotion. Being such a widespread problem, in recent years researchers are looking for techniques to artificially restore sensory feedback to people with neuropathy. One of the most innovative solution consists in the use of TENS (Transcutaneous Electrical Nerve Stimulation) to stimulate non-invasively the peripheral nerves. Although encouraging results to restore sensory feedback have been obtained using this technique, the choice of stimulation parameters remains a very topical problem given the multidimensional space of possible parameters to be explored before choosing the optimal setting.
The goal of my thesis is therefore to develop a closed-loop system based on AI-algorithm that allows an automatic and efficient identification of stimulation parameters for neuroprosthetic applications
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