The demonstration presents a wireless system to control video games with user hand movements. Muscles activity is detected by applying the Average Threshold Crossing (ATC) technique to the surface ElectroMyoGraphic (sEMG) signals acquired from two sets of electrodes on the user forearm. Three hand movements and an idle state are classified in real-time on a computer by implementing a Neural Network (NN) feeded with the acquired ATC values, with accuracies above 97%. Recognized gestures are then mapped to keyboard inputs to control the maneuvers of a game character.

Live Demonstration: Event-Driven Hand Gesture Recognition for Wearable Human-Machine Interface / Becchio, Martina; Voster, Niccolo; Prestia, Andrea; Mongardi, Andrea; Rossi, Fabio; Ros, Paolo Motto; Roch, Massimo Ruo; Martina, Maurizio; Demarchi, Danilo. - ELETTRONICO. - (2021), pp. 1-1. ((Intervento presentato al convegno 2021 IEEE Biomedical Circuits and Systems Conference (BioCAS) tenutosi a Berlin (Germany) nel 7-9 Ottobre 2021 [10.1109/BioCAS49922.2021.9644647].

Live Demonstration: Event-Driven Hand Gesture Recognition for Wearable Human-Machine Interface

Becchio, Martina;Prestia, Andrea;Mongardi, Andrea;Rossi, Fabio;Ros, Paolo Motto;Roch, Massimo Ruo;Martina, Maurizio;Demarchi, Danilo
2021

Abstract

The demonstration presents a wireless system to control video games with user hand movements. Muscles activity is detected by applying the Average Threshold Crossing (ATC) technique to the surface ElectroMyoGraphic (sEMG) signals acquired from two sets of electrodes on the user forearm. Three hand movements and an idle state are classified in real-time on a computer by implementing a Neural Network (NN) feeded with the acquired ATC values, with accuracies above 97%. Recognized gestures are then mapped to keyboard inputs to control the maneuvers of a game character.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2947836