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Live Demonstration: Low Power Embedded System for Event-Driven Hand Gesture Recognition


3D Reconstruction in Canonical

Class Agnostic Image Common Ob
Abstract


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 gestures .

KeyWords
Gesture recognition , Power demand , Embedded systems , Microcontrollers , Neurocontrollers , Neural networks



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