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A standalone computing system to classify human foot movements using machine learning techniques for ankle-foot

Sachin Negi1,2, Neeraj Sharma1

  • 1School of Biomedical Engineering, Indian Institute of Technology (BHU) Varanasi, Varanasi, India.

Computer Methods in Biomechanics and Biomedical Engineering
|December 6, 2021
PubMed
Summary
This summary is machine-generated.

This study demonstrates TinyML on an Arduino Nano 33 BLE for accurate, real-time foot movement classification using EMG signals, ideal for prosthetic leg control. The system achieved high accuracy and fast processing, outperforming FMG signals.

Keywords:
Ankle-foot prosthesisArduino Nano 33 BLE controllerTinyMLelectromyographyforcemyography

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Area of Science:

  • Biomedical Engineering
  • Machine Learning
  • Prosthetics

Background:

  • Accurate real-time prediction of foot movements is crucial for advanced prosthetic limb control.
  • Existing systems often face challenges with speed, size, and accuracy in prosthetic applications.

Purpose of the Study:

  • To develop and evaluate machine learning techniques for classifying six distinct foot movements (dorsiflexion, plantarflexion, inversion, eversion, medial rotation, lateral rotation).
  • To design a real-time, standalone computing system for prosthetic leg control using Electromyography (EMG) and ForceMyography (FMG) signals.
  • To implement and assess a TinyML algorithm on an Arduino Nano 33 BLE controller for efficient, embedded foot movement classification.

Main Methods:

  • Acquisition of EMG and FMG signals from leg muscles (tibialis anterior, medial gastrocnemius, lateral gastrocnemius, peroneus longus).
  • Real-time classification using machine learning on Raspberry Pi, including Linear Discriminant Analysis (LDA), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Support Vector Classification (SVC).
  • Implementation of a TinyML algorithm on an Arduino Nano 33 BLE for real-time classification and validation on a transtibial amputee.

Main Results:

  • Offline classification using Raspberry Pi achieved >99.5% accuracy with EMG signals via LDA, LR, KNN, and SVC.
  • LDA demonstrated excellent real-time performance for all foot movement classes.
  • The TinyML algorithm on Arduino Nano 33 BLE achieved real-time classification (8.5ms) with zero misclassifications.
  • EMG signal-based classification accuracy significantly surpassed FMG signal-based accuracy.
  • Successful real-time classification of three foot movement classes in a transtibial amputee.

Conclusions:

  • TinyML on an Arduino Nano 33 BLE microcontroller offers a fast, accurate, and compact solution for real-time prosthetic leg control.
  • The system's efficiency and high accuracy make it highly advantageous for practical prosthetic applications.
  • EMG signal processing with TinyML presents a promising approach for enhancing prosthetic limb functionality.