Andrea Mongardi, Fabio Rossi, P. Ros, A. Sanginario, M. R. Roch, M. Martina, D. Demarchi
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Live Demonstration: Low Power Embedded System for Event-Driven Hand Gesture Recognition
This demonstration presents a low power embedded system to classify hand movements. The surface ElectroMyo-Graphic (sEMG) signals acquired from the forearm are preprocessed using the Average Threshold Crossing (ATC) event-driven technique, which heavily reduces hardware complexity and power consumption. The quasi-digital output is sent to an ultra low power microcontroller, which implements a fully-connected Neural Network (NN). A small Arduino-based tank is used to demonstrate the real-time behavior of the system and to show the correctness of the predicted gestures1.
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