Alberto Giammarioli
Development of a Neural Interface for Intuitive and Robust Control of a Neuroorthosis in Daily Life Using High-Density Surface Electromyography.
Rel. Luca Mesin, Alessandro Del Vecchio, Dominik Braun. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2026
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Abstract
Surface electromyography (sEMG) uses electrodes applied on the skin to capture the electrical activity of the muscles. One of its most common applications over the last decades has been myoelectric control: this electrical activity results in a signal that can be decoded and used as input for the control of an assisting device (e.g. prosthesis, orthosis, exoskeleton). The Broader goal of the project to which this thesis belongs is the single-degree-of-freedom (1-DoF) myoelectric control of a hand orthosis for patients with spinal cord injury (SCI) causing impairment to their hands: capturing the residual activity of the forearm muscles, understanding whether the user wants to open or close the hand, and using that information to drive the orthosis actuation.
The present thesis focuses on the decoding side of that pipeline: the design and experimental validation of an algorithm that, given the live forearm sEMG, reliably detects open–close intent while robustly suppressing false activations caused by non-target movements, all across a representative range of arm postures
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