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Related Experiment Video

Updated: Jun 5, 2025

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis
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Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

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Continuous-Context, User-Independent, Real-Time Intent Recognition for Powered Lower-Limb Prostheses.

Krishan Bhakta1, Jairo Maldonado-Contreras2,3, Jonathan Camargo4,5

  • 1Woodruff School of Mechanical Engineering, Georgia Institute of Technology, 813 Ferst Drive NW, Atlanta, GA 30332.

Journal of Biomechanical Engineering
|December 12, 2024
PubMed
Summary

A new user-independent system accurately estimates walking speed and slope for powered prostheses. This technology offers intuitive assistance for individuals with transfemoral amputation during community ambulation.

Keywords:
intent recognitionmachine learningpowered prostheticsreal-time controlwearable robotics

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Area of Science:

  • Robotics
  • Biomechanics
  • Rehabilitation Engineering

Background:

  • Community ambulation is vital for a healthy lifestyle but challenging for individuals with limb loss.
  • Powered prostheses require accurate environmental context estimation (e.g., speed, slope) for intuitive user assistance.
  • Existing systems often rely on user-dependent models, limiting adaptability.

Purpose of the Study:

  • To develop and validate a user-independent, multicontext intent recognition system for powered prostheses.
  • To accurately estimate real-time walking speed and slope in diverse ambulation scenarios.
  • To provide an open-source dataset for advancing powered prosthesis research.

Main Methods:

  • Developed a user-independent intent recognition system deployed on an Open Source Leg (OSL).
  • Recruited 11 individuals with transfemoral amputation for system testing and validation.
  • Collected real-time ambulation data across various speeds and slopes.

Main Results:

  • User-independent (IND) model performance was statistically similar to user-dependent (DEP) models in real-time.
  • IND models showed no performance degradation compared to offline counterparts.
  • Achieved a mean absolute error of ~0.09 m/s for speed and ~0.95 degrees for slope estimation.

Conclusions:

  • User-independent models are effective for estimating speed and slope in powered prostheses.
  • The developed system enables seamless and intuitive prosthetic assistance during varied community ambulation.
  • The open-source dataset will foster further research in real-world powered prosthesis applications.