Electromiographic signal processing using embedded artificial intelligenceAn adaptive filtering approach

  1. Daniel Proaño-Guevara 1
  2. Xiomara Blanco Valencia 2
  3. Paul D. Rosero-Montalvo 3
  4. Diego H. Peluffo Ordóñez 4
  1. 1 Universidade de Lisboa
    info

    Universidade de Lisboa

    Lisboa, Portugal

    ROR https://ror.org/01c27hj86

  2. 2 Universidad Internacional de La Rioja
    info

    Universidad Internacional de La Rioja

    Logroño, España

    ROR https://ror.org/029gnnp81

  3. 3 IT University of Copenhagen
    info

    IT University of Copenhagen

    Copenhague, Dinamarca

    ROR https://ror.org/02309jg23

  4. 4 Mohammed VI Polytechnic University, (Morocco)
Zeitschrift:
IJIMAI

ISSN: 1989-1660

Datum der Publikation: 2022

Ausgabe: 7

Nummer: 5

Seiten: 40-50

Art: Artikel

DOI: 10.9781/IJIMAI.2022.08.009 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Andere Publikationen in: IJIMAI

Zusammenfassung

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.