Neuromorphic Implementation of a Recurrent Neural Network for EMG Classification

Wearable sensor devices are disrupting healthcare technologies with a rapid increase of systems able to perform continuous monitoring of physiological data. These systems however typically produce large amounts of data that needs to be processed in real-time. Neuromorphic processors represent a promising class of devices that can be directly interfaced with the sensors to extract temporal data-streams in real-time with very low-power consumption. In this paper we present an application of such a neuromorphic approach for classifying electromyography (EMG) signals, that can be useful for designing compact wearable solutions for neuroprosthetic control. As the neuromorphic processor comprises configurable spiking neurons and adaptive synapses, we propose to use a spiking recurrent neural network (SRNN) for classifying the spatio-temporal data derived from the EMG signals. We performed a thorough investigation on the performance of the network implemented on the chip, by evaluating the classification performance of two hardware-friendly spike-based read-out models: a rate-based state distance model, and a spiking-time-dependent plasticity (STDP) learning model. The results were then compared with a classical machine learning approach based on the Support Vector Machine (SVM) method. Our results show how the classification accuracy of the state distance method is above 75%, which is even better than the SVM results; while the STDP learning rule only achieved 60% accuracy.