Neuro-fuzzy Modeling of Multi-field Surface Neuroprostheses for Hand Grasping [electronic resource] /
Imatz Ojanguren, Eukene.author.
This thesis presents a novel neuro-fuzzy modeling approach for grasp neuroprostheses. At first, it offers a detailed study of discomfort due to the application of Functional Electrical Stimulation to the upper limb. Then, it discusses briefly previous methods to model hand movements induced by FES with the purpose of introducing the new modeling approach based on intelligent systems. This approach is thoroughly described in the book, together with the proposed application to induce hand and finger movements by means of a surface FES system based on multi-field electrodes. The validation tests, carried out on both healthy and neurologically impaired subjects, demonstrate the efficacy of the proposed modeling method. All in all, the book proposes an innovative system based on fuzzy neural networks that is expected to improve the design and validation of advanced control systems for non-invasive grasp neuroprostheses.
 Introduction -- Phisiology -- Functional Electrical Stimulation -- Discomfort analysis -- Pain-maps -- FES modeling -- Computational Intelligence -- Neuro-fuzzy model -- Neuro-fuzzy model -- Conclusions.
This thesis presents a novel neuro-fuzzy modeling approach for grasp neuroprostheses. At first, it offers a detailed study of discomfort due to the application of Functional Electrical Stimulation to the upper limb. Then, it discusses briefly previous methods to model hand movements induced by FES with the purpose of introducing the new modeling approach based on intelligent systems. This approach is thoroughly described in the book, together with the proposed application to induce hand and finger movements by means of a surface FES system based on multi-field electrodes. The validation tests, carried out on both healthy and neurologically impaired subjects, demonstrate the efficacy of the proposed modeling method. All in all, the book proposes an innovative system based on fuzzy neural networks that is expected to improve the design and validation of advanced control systems for non-invasive grasp neuroprostheses.
Control engineering.
Biomedical engineering.
Computational intelligence.
Biomechanics.
Signal processing.
Image processing.
Speech processing systems.
Control and Systems Theory.
Biomedical Engineering and Bioengineering.
Computational Intelligence.
Biomechanics.
Signal, Image and Speech Processing.