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Related Experiment Video

Updated: Jun 22, 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

A Deep Learning Framework for End-to-End Control of Powered Prostheses.

Christoph P O Nuesslein¹, Aaron J Young¹

¹Institute for Robotics and Intelligent Machines and the Department of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA.

IEEE Robotics and Automation Letters

|February 27, 2025

Summary

This summary is machine-generated.

Deep learning models can control active lower-limb prostheses, eliminating manual tuning. A temporal convolutional network (TCN) demonstrated user-independent control for transfemoral amputees across various locomotion modes.

Keywords:

Prosthesis deep learning end-to-end control torque estimation

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Related Experiment Videos

Last Updated: Jun 22, 2026

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

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Published on: July 22, 2014

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

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Published on: November 6, 2015

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

Published on: January 7, 2019

Area of Science:

Biomedical Engineering
Robotics
Artificial Intelligence

Background:

Traditional control of active lower-limb prostheses relies on hand-tuned parameters and complex state machines.
Deep learning presents a novel approach to end-to-end control, potentially simplifying prosthesis operation and improving adaptability.

Purpose of the Study:

To investigate the efficacy of a deep learning model, specifically a temporal convolutional network (TCN), for controlling an open-source powered knee-ankle prosthesis (OSL).
To assess the model's ability to generate user- and mode-independent joint torques, eliminating the need for conventional control strategies.

Main Methods:

Collected sensor data and commanded torque from 12 transfemoral amputees using an OSL across five locomotion modes.
Trained a TCN to estimate stance phases and produce knee and ankle torques, comparing performance against an expert-tuned finite state machine controller.
Evaluated model performance using Root Mean Square Error (RMSE) and analyzed adaptation to walking speed and slope variations.

Main Results:

The TCN achieved mode- and user-independent knee and ankle torque control with RMSEs of 0.154 ± 0.06 and 0.106 ± 0.06 Nm/kg, respectively.
Training on mode-specific data significantly reduced RMSE for stair descent.
The TCN demonstrated adaptability to changes in walking speed and slope.

Conclusions:

Deep learning, particularly TCNs, can effectively replace heuristic state machines and mode classification in lower-limb prosthesis control.
This approach has the potential to significantly reduce or eliminate the need for manual prosthesis assistance tuning, enhancing user experience and performance.