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Estimation of User-Applied Isometric Force/Torque Using Upper Extremity Force Myography.

Maram Sakr1, Xianta Jiang1, Carlo Menon1

  • 1Menrva Research Group, Schools of Mechatronic Systems and Engineering Science, Simon Fraser University, Burnaby, BC, Canada.

Frontiers in Robotics and AI
|January 27, 2021
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Summary

This study shows Force Myography (FMG) using armbands with force-sensing resistors can estimate hand force and torque. This non-invasive technique shows promise for human-machine interaction applications.

Keywords:
force myographyhand force/torque estimationhuman-machine interactionmulti-output regressionwearable sensors

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Area of Science:

  • Biomedical Engineering
  • Human-Machine Interaction
  • Wearable Technology

Background:

  • Accurate hand force estimation is crucial for human-machine interaction, monitoring, and control.
  • Force Myography (FMG) offers a non-invasive method by measuring muscle volume changes.
  • Previous methods often require direct contact or are limited in scope.

Purpose of the Study:

  • To investigate the feasibility of using Force-Sensing Resistors (FSRs) on armbands for estimating multi-Degree-of-Freedom (DoF) isometric hand force and torque.
  • To evaluate the effectiveness of a two-stage regression strategy for enhancing FMG-based estimation accuracy.
  • To analyze the influence of sensor placement and spatial coverage on estimation performance.

Main Methods:

  • Nine participants exerted isometric forces and torques along three axes, individually and combined.
  • Sixty FSRs embedded in four armbands recorded FMG signals.
  • A 6-DoF load cell provided ground truth measurements.
  • A two-stage regression model utilizing General Regression Neural Network (GRNN), Support Vector Regression (SVR), and Random Forest Regression (RF) was implemented.

Main Results:

  • The study achieved R-squared accuracies of 0.83 for 3-DoF force, 0.84 for 3-DoF torque, and 0.77 for combined 6-DoF force and torque estimation.
  • Cross-trial evaluation demonstrated the robustness of the FMG estimation method.
  • Analysis indicated that sensor placement and spatial coverage impact estimation performance.

Conclusions:

  • Force Myography (FMG) using FSRs presents a viable and promising approach for estimating multi-DoF isometric hand forces and torques.
  • The developed two-stage regression strategy significantly enhances estimation accuracy.
  • This technology holds potential for advancing non-invasive human-machine interfaces and control systems.