A Low-Power Embedded System for Real-Time sEMG based Event-Driven Gesture Recognition

Latency and power consumption management are present-day hot topics for wearable devices and IoT applications. This paper presents the implementation of a low power system for hand movement recognition, based on surface ElectroMyo-Graphic (sEMG) signals, suitable for human-machine interface. Every time the sEMG signal crosses a predefined threshold an event is generated, implementing the Average Threshold Crossing (ATC) technique directly on-boards on the subject arm. Resulting quasi-digital signals, averaged over a fixed time window, are sent to an ARM Cortex-M4F processor which implements a fully-connected Neural Network (NN) able to recognize six different gestures from only three input channels. Dataset creation has involved 25 healthy people, each one performing five movements within five repeated sessions. The NN has been trained using the per-subject holdout validation method, obtaining an accuracy of 96.34%. With a maximum latency of 8.5 ms and an average power consumption of only 0.8 mW at full recognition rate, the proposed NN implementation shows promising results.