Camilla Arnaud
Development of a myoelectric prosthesis control based on deep learning. Optimisation of the neural network parameters and EMG detection system.
Rel. Marco Gazzoni, Emanuele Gruppioni, Giacinto Luigi Cerone. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2023
Abstract
The most commonly used commercially available prostheses for transradial amputees offer a limited number of movements of the prosthetic hand. They are old-design prostheses with only one possible closure movement controlled using a direct control strategy. These prostheses are single-joint devices with a pinch that involves the movement of all the five fingers at the same time, with no option for individual finger movement. The control strategy relies on the remaining muscular activity from the stump in an easy and intuitive way for the user. These devices are very easy to control and durable to be used in everyday activities, but they do not provide the possibility of moving the prosthesis in a more natural and physiological way.
Having a multiarticulate prosthetic device that allows a higher number of gestures remaining easy and intuitive to control would be a great achievement for transradial amputees
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