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EMG-Based Gestures Classification Using a Mixed-Signal Neuromorphic Processing System


Ma, Yongqiang; Chen, Badong; Ren, Pengju; Zheng, Nanning; Indiveri, Giacomo; Donati, Elisa (2020). EMG-Based Gestures Classification Using a Mixed-Signal Neuromorphic Processing System. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 10(4):578-587.

Abstract

The rapid increase of wearable sensor devices poses new challenges for implementing continuous real-time processing of physiological data. Neuromorphic sensory-processing devices can enable both the measurement of bio-signals and their processing locally in compact embedded wearable systems. In particular, mixed-signal spiking neural networks implemented on neuromorphic processors can be integrated directly with the sensors to extract temporal data-streams in real-time with very low power consumption. In this work, we present a neuromorphic approach for classifying spatio-temporal data from electromyography (EMG) signals, which paves the way toward the realization of compact wearable solutions for neuroprosthetic control. Here we extend previously proposed delta-encoding methods to transform bio-signals into spike trains and use a spiking Recurrent Neural Network (SRNN) architecture to extract features from them. The SRNN was first simulated in software to find the optimal set of hyperparameters, and then validated on the neuromorphic hardware, with a difference in the performance of less than 2%. We describe how biologically plausible mechanisms such as Spike-Timing Dependent Plasticity (STDP) and soft Winner-Take-All (WTA) networks can be exploited to classify the EMG signals and show how their combined use in EMG data classification achieves competitive results with different datasets. Specifically, the classification performance for the Roshambo EMG dataset, which has three different classes, is above 85%, and for the basic finger movements dataset from the Ninapro database, which has eight different classes, reaches 55% accuracy.

Abstract

The rapid increase of wearable sensor devices poses new challenges for implementing continuous real-time processing of physiological data. Neuromorphic sensory-processing devices can enable both the measurement of bio-signals and their processing locally in compact embedded wearable systems. In particular, mixed-signal spiking neural networks implemented on neuromorphic processors can be integrated directly with the sensors to extract temporal data-streams in real-time with very low power consumption. In this work, we present a neuromorphic approach for classifying spatio-temporal data from electromyography (EMG) signals, which paves the way toward the realization of compact wearable solutions for neuroprosthetic control. Here we extend previously proposed delta-encoding methods to transform bio-signals into spike trains and use a spiking Recurrent Neural Network (SRNN) architecture to extract features from them. The SRNN was first simulated in software to find the optimal set of hyperparameters, and then validated on the neuromorphic hardware, with a difference in the performance of less than 2%. We describe how biologically plausible mechanisms such as Spike-Timing Dependent Plasticity (STDP) and soft Winner-Take-All (WTA) networks can be exploited to classify the EMG signals and show how their combined use in EMG data classification achieves competitive results with different datasets. Specifically, the classification performance for the Roshambo EMG dataset, which has three different classes, is above 85%, and for the basic finger movements dataset from the Ninapro database, which has eight different classes, reaches 55% accuracy.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Scopus Subject Areas:Physical Sciences > Electrical and Electronic Engineering
Uncontrolled Keywords:Electrical and Electronic Engineering
Language:English
Date:1 December 2020
Deposited On:16 Feb 2021 08:50
Last Modified:17 Feb 2021 21:02
Publisher:Institute of Electrical and Electronics Engineers
ISSN:2156-3357
OA Status:Closed
Publisher DOI:https://doi.org/10.1109/jetcas.2020.3037951
Project Information:
  • : FunderH2020
  • : Grant ID753470
  • : Project TitleNEPSpiNN - Neuromorphic EMG Processing with Spiking Neural Networks
  • : FunderH2020
  • : Grant ID732170
  • : Project TitleCResPace - Adaptive Bio-electronics for Chronic Cardiorespiratory Disease
  • : FunderH2020
  • : Grant ID724295
  • : Project TitleNeuroAgents - Neuromorphic Electronic Agents: from sensory processing to autonomous cognitive behavior

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