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Development of a Novel Task-oriented Rehabilitation Program using a Bimanual Exoskeleton Robotic Hand

Development of a Novel Task-oriented Rehabilitation Program using a Bimanual Exoskeleton Robotic Hand

Published on: May 20, 2020

Data-Driven Predictive Control of Exoskeleton for Hand Rehabilitation with Subspace Identification.

Erkan Kaplanoglu¹, Gazi Akgun²

¹Department of Engineering Management & Technology, University of Tennessee at Chattanooga, Chattanooga, TN 37403, USA.

Sensors (Basel, Switzerland)

|October 14, 2022

Summary

A new data-driven predictive control (DDPC) method offers safe and customizable rehabilitation using hand exoskeletons. This model-free approach enhances repetitive therapy tasks by managing system constraints effectively.

Keywords:

DDPC hand rehabilitation subspace identification

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Area of Science:

Robotics
Rehabilitation Engineering
Control Systems

Background:

Hand exoskeletons are crucial for rehabilitation.
Existing control methods face challenges in customization and safety for repetitive tasks.

Purpose of the Study:

To propose a novel data-driven predictive control (DDPC) method for hand exoskeletons.
To enhance the safety and customizability of rehabilitation robotics.

Main Methods:

Developed a model-free control algorithm using past system data.
Integrated state constraints directly into the controller design.
Implemented and tested the DDPC on a designed hand rehabilitation system.

Main Results:

The DDPC method demonstrated feasibility and efficiency in real-time experiments.
The approach successfully managed constraints for safe, repetitive rehabilitation tasks.
The controller allowed for easy customization of therapy parameters.

Conclusions:

The proposed DDPC is an effective control strategy for hand exoskeleton rehabilitation.
This method addresses key challenges in rehabilitation robotics, improving patient therapy.
DDPC offers a promising solution for personalized and safe robotic rehabilitation.