Hardware Implementation for Lower Limb Surface EMG Measurement and Analysis Using Explainable AI for Activity Recognition

Ankit Vijayvargiya, Puneet Singh, Rajesh Kumar, Nilanjan Dey

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)

Abstract

Electromyography (EMG) signals are gaining popularity for several biomedical applications, including pattern recognition, disease detection, human-machine interfaces, medical image processing, and robotic limb or exoskeleton fabrication. In this study, a two-channel data acquisition system for measuring EMG signals is proposed for human lower limb activity recognition. Five leg activities have been accomplished to measure EMG signals from two lower limb muscles to validate the developed hardware. Five subjects (three males and two females) were chosen to acquire EMG signals during these activities. The raw EMG signal was first denoised using a hybrid of Wavelet Decomposition with Ensemble Empirical Mode Decomposition (WD-EEMD) approach to classify the recorded EMG dataset. Then, eight time-domain (TD) features were extracted using the overlapping windowing technique. An investigation into the comparative effectiveness of several classifiers is presented, although it was hard to distinguish how the classifiers predicted the activities. Having a trustworthy explanation for the outcomes of these classifiers would be quite beneficial overall. An approach known as explainable artificial intelligence (XAI) was introduced to produce trustworthy predictive modeling results and applied the XAI technique known as local interpretable model-agnostic explanations (LIME) to a straightforward human interpretation. LIME investigates how extracted features are anticipated and which features are most responsible for each action. The accuracy of the extra tree classifier gives the highest accuracy of the other studied algorithms for identifying different human lower limb activities from sEMG signals.

Original languageEnglish
Article number2004909
JournalIEEE Transactions on Instrumentation and Measurement
Volume71
DOIs
Publication statusPublished - 2022
Externally publishedYes

Keywords

  • Electromyography (EMG) signal acquisition system
  • explainable artificial intelligence (XAI)
  • local interpretable model-agnostic explanations (LIME)
  • lower limb activity recognition
  • machine learning (ML)
  • signal processing

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering

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