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Related Experiment Video

Updated: Aug 12, 2025

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

Characterizing Prosthesis Control Fault during Human-Prosthesis Interactive Walking Using Intrinsic Sensors.

Amirreza Naseri^1,2, Ming Liu^1,2, I-Chieh Lee^1,2

¹UNC/NCSU Department of Biomedical Engineering, NC State University, Raleigh, NC 27695 USA.

IEEE Robotics and Automation Letters

|January 30, 2023

Summary

This study developed a method to detect faults in robotic transfemoral prostheses using machine learning. It ensures user safety by identifying control errors for more robust wearable robot design.

Keywords:

Failure Detection and Recovery Physical Human-Robot Interaction Prosthetics and Exoskeletons Safety in HRI

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Last Updated: Aug 12, 2025

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Published on: July 22, 2014

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Area of Science:

Robotics
Biomechanics
Machine Learning

Background:

Human-robot interaction is crucial for wearable lower limb robots.
Internal robot faults require systematic study for user safety and device robustness.

Purpose of the Study:

To present a methodology for characterizing robotic transfemoral prosthesis behavior during internal faults.
To identify data sources for accurate prosthesis fault detection.

Main Methods:

Emulated prosthesis control faults during level-ground walking.
Examined prosthesis sensor measurements and features for fault detection.
Used One-Class Support Vector Machine (OCSVM) and Mahalanobis Distance (MD) classifiers.

Main Results:

OCSVM achieved 85.7% sensitivity and 1.7% false alarm rate.
Fault detection time averaged 147.6 ms.
Identified optimal features for distinguishing faulty from normal walking conditions.

Conclusions:

Machine learning effectively detects prosthesis control faults using intrinsic sensor data.
The study provides a procedure for analyzing human-robot fault tolerance.
Informs future designs for robust prosthetic control systems.