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Updated: May 22, 2026

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

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

Published on: November 6, 2015

Patient training for functional use of pattern recognition-controlled prostheses.

Ann M Simon¹, Blair A Lock, Kathy A Stubblefield

¹Center for Bionic Medicine, Rehabilitation Institute of Chicago, Chicago, IL.

Journal of Prosthetics and Orthotics : JPO

|May 8, 2012

Summary

Effective training is key for pattern recognition control systems in myoelectric prostheses. Prosthesis-guided training (PGT) aids recalibration, potentially increasing prosthesis wear time and function for amputees.

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Area of Science:

Biomedical Engineering
Rehabilitation Technology
Human-Computer Interaction

Background:

Pattern recognition control systems offer improved reliability for myoelectric prostheses in upper-limb amputees.
Effective patient training is crucial for successful implementation of these advanced prosthetic systems.

Purpose of the Study:

To describe a structured training protocol for pattern recognition control of myoelectric prostheses.
To introduce and evaluate prosthesis-guided training (PGT) as a method to enhance prosthesis use and recalibration.

Main Methods:

User training progresses from fundamental concepts to practical prosthesis control, use, and maintenance across various degrees of freedom.
Case studies illustrate the training stages.
Prosthesis-guided training (PGT) is presented as a self-initiated recalibration tool.

Main Results:

PGT offers a simple method for recalibrating pattern recognition-controlled prostheses.
PGT has the potential to increase functional use times and overall prosthesis wear time.

Conclusions:

A comprehensive training approach, including PGT, can facilitate the transition of pattern recognition control systems from laboratory settings to home environments.
This training strategy aims to unlock the full potential of pattern recognition control for individuals with upper-limb amputations.