Electromiographic Signal Processing Using Embedded Artificial Intelligence: An Adaptive Filtering Approach.

Daniel Proaño Guevara; Xiomara Blanco Valencia; Paul D. Rosero Montalvo; Diego H. PeluffoOrdóñez

doi:10.9781/ijimai.2022.08.009

Authors

Daniel Proaño Guevara University of Lisbon
Xiomara Blanco Valencia Universidad Internacional De La Rioja
Paul D. Rosero Montalvo IT University of Copenhagen
Diego H. PeluffoOrdóñez Université Mohammed VI Polytechnique

DOI:

https://doi.org/10.9781/ijimai.2022.08.009

Keywords:

Adaptive Filters, Artificial Intelligence, Edge Computing, Digital Signal Processor (DSP), Signal, Data Pre-processing

Supporting Agencies

The authors acknowledges the valuable support given by the SDAS Research Group (https://sdas-group.com/ –Accesed on: 11-Jul-2022).

Abstract

In recent times, Artificial Intelligence (AI) has become ubiquitous in technological fields, mainly due to its ability to perform computations in distributed systems or the cloud. Nevertheless, for some applications -as the case of EMG signal processing- it may be highly advisable or even mandatory an on-the-edge processing, i.e., an embedded processing methodology. On the other hand, sEMG signals have been traditionally processed using LTI techniques for simplicity in computing. However, making this strong assumption leads to information loss and spurious results. Considering the current advances in silicon technology and increasing computer power, it is possible to process these biosignals with AI-based techniques correctly. This paper presents an embedded-processing-based adaptive filtering system (here termed edge AI) being an outstanding alternative in contrast to a sensor-computer- actuator system and a classical digital signal processor (DSP) device. Specifically, a PYNQ-Z1 embedded system is used. For experimental purposes, three methodologies on similar processing scenarios are compared. The results show that the edge AI methodology is superior to benchmark approaches by reducing the processing time compared to classical DSPs and general standards while maintaining the signal integrity and processing it, considering that the EMG system is not LTI. Likewise, due to the nature of the proposed architecture, handling information exhibits no leakages. Findings suggest that edge computing is suitable for EMG signal processing when an on-device analysis is required.

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