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Related Experiment Video

Updated: Jun 9, 2026

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

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

Published on: July 22, 2014

Volitional control of a prosthetic knee using surface electromyography.

Kevin H Ha¹, Huseyin Atakan Varol, Michael Goldfarb

¹Department of Mechanical Engineering, Vanderbilt University, Nashville, TN 37235, USA. kevin.h.ha@vanderbilt.edu

IEEE Transactions on Bio-Medical Engineering

|September 1, 2010

Summary

Researchers developed a new volitional control method for powered knee prostheses using surface electromyography (EMG) signals. This advanced system allows users to control prosthetic knee movement during non-weight-bearing activities with improved accuracy.

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Therapy Interventions for Upper Limb Amputees Undergoing Selective Nerve Transfers

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Published on: October 29, 2021

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Last Updated: Jun 9, 2026

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Therapy Interventions for Upper Limb Amputees Undergoing Selective Nerve Transfers

Therapy Interventions for Upper Limb Amputees Undergoing Selective Nerve Transfers

Published on: October 29, 2021

Area of Science:

Biomedical Engineering
Rehabilitation Robotics
Prosthetics and Orthotics

Background:

Powered knee prostheses aim to restore natural gait and function for amputees.
Volitional control during non-weight-bearing activities like sitting remains a challenge.
Existing methods often struggle to accurately interpret user intent from residual limb muscle signals.

Purpose of the Study:

To present a novel method for volitional control of a powered knee prosthesis during non-weight-bearing tasks.
To utilize an impedance control framework for natural joint behavior.
To decode user intent from surface electromyography (EMG) using advanced pattern recognition.

Main Methods:

Implemented an impedance framework to program prosthesis stiffness and damping.
Commanded knee joint movement via stiffness set-point angle.
Utilized quadratic discriminant analysis and principal component analysis to interpret EMG patterns from hamstring and quadriceps muscles.
Tested the system on three transfemoral amputees performing trajectory tracking tasks.

Main Results:

The EMG-based volitional control system was successfully implemented on transfemoral amputees.
Average root mean square trajectory tracking error for the prosthetic knee was 6.2°.
The prosthetic knee performance was comparable to the intact knee's average error of 5.2°.

Conclusions:

The proposed method enables volitional control of powered knee prostheses during non-weight-bearing activities.
Advanced EMG signal processing effectively decodes user intent for prosthetic knee movement.
This approach shows promise for enhancing the functionality and user experience of lower-limb prosthetics.