Lobov, S.Krilova, N.Kastalskiy, I.Kazantsev, V.Makarov Slizneva, ValeriyKrebs, H. I.Pedotti, A.Faisal, A.2023-06-182023-06-182016978-989-758-204-2http://www.scitepress.org/DigitalLibrary/PublicationsDetail.aspx?ID=0LFgGcWLOkA%3d&t=1https://hdl.handle.net/20.500.14352/249124th International Congress on Neurotechnology, Electronics and Informatics (NEUROTECHNIX)Surface electromyographic (sEMG) signals represent a superposition of the motor unit action potentials that can be recorded by electrodes placed on the skin. Here we explore the use of an easy wearable sEMG bracelet for a remote interaction with a computer by means of hand gestures. We propose a human-computer interface that allows simulating “mouse” clicks by separate gestures and provides proportional control with two degrees of freedom for flexible movement of a cursor on a computer screen. We use an artificial neural network (ANN) for processing sEMG signals and gesture recognition both for mouse clicks and gradual cursor movements. At the beginning the ANN goes through an optimized supervised learning using either rigid or fuzzy class separation. In both cases the learning is fast enough and requires neither special measurement devices nor specific knowledge from the end-user. Thus, the approach enables building of low-budget user-friendly sEMG solutions.engbook parthttp://dx.doi.org/10.5220/0006033300570064http://www.scitepress.org/restricted access004.8ElectromyographyHuman-Computer InterfacePattern ClassificationArtificial Neural Networks.Inteligencia artificial (Informática)1203.04 Inteligencia Artificial