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Updated: Apr 19, 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

Modulation of grasping force in prosthetic hands using neural network-based predictive control.

Cristian F Pasluosta¹, Alan W L Chiu

¹Electronics Core-Medical Device Solutions, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.

Methods in Molecular Biology (Clifton, N.J.)

|December 16, 2014

Summary

This study introduces a neural network controller for prosthetic hands, addressing challenges in precise electromechanical control. Simulations show this approach is feasible for daily living activities.

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Area of Science:

Biomedical Engineering
Robotics
Artificial Intelligence

Background:

Prosthetic hand control faces challenges due to complex electromechanical nonlinearities.
Existing model-based controllers may struggle with these intricate dynamics.

Purpose of the Study:

To implement a neural network-based predictive control system for prosthetic hand actuation.
To model the complex finger dynamics of prosthetic devices using neural networks.

Main Methods:

Development of a neural network-based predictive control system.
Utilizing neural network-based modeling to capture finger dynamics.
Simulation of control system performance in daily living scenarios.

Main Results:

Demonstrated feasibility of the neural network-based predictive control system.
Successfully modeled complex electromechanical nonlinearities of the prosthetic hand.
Simulations indicated effective performance in realistic daily activities.

Conclusions:

Neural network-based predictive control is a viable solution for precise prosthetic hand operation.
This technique effectively addresses the nonlinearities inherent in prosthetic devices.
The proposed system shows promise for enhancing prosthetic hand functionality in daily life.