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A clinical set-up tool (CST) for rapid stimulator programming.

P Tresadern¹, S B Thies, L P J Kenney

¹Centre for Rehabilitation and Human Performance Research, University of Salford, United Kingdom M6 6PU. p.tresadern@salford.ac.uk

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|November 16, 2007

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This study introduces an intuitive method for programming Functional Electrical Stimulation (FES) hardware using a Finite State Machine (FSM) interface. Clinicians can train classifiers with real-time motion data for optimized FES parameter uploads.

Area of Science:

Biomedical Engineering
Rehabilitation Technology
Neuroprosthetics

Background:

Functional Electrical Stimulation (FES) requires precise programming for effective therapeutic or assistive applications.
Current FES programming methods can be complex and time-consuming, hindering widespread clinical adoption.
Developing intuitive and efficient FES programming tools is crucial for advancing neurorehabilitation.

Purpose of the Study:

To present an intuitive and user-friendly approach for programming Functional Electrical Stimulation (FES) hardware.
To enable clinicians to easily define, train, and optimize FES stimulation sequences.
To facilitate the rapid deployment of customized FES protocols for individual patient needs.

Main Methods:

A Finite State Machine (FSM) model is utilized to define stimulation sequences.

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An easy-to-use graphical interface allows for FSM creation and modification.

Real-time motion data from an internal accelerometer are streamed from a USB-equipped stimulator to a PC.

Clinicians label accelerometer data via key presses to train simple classifiers.

Optimal stimulation parameters are quantitatively determined and uploaded back to the stimulator.

Main Results:

Demonstrated an intuitive method for FES hardware programming.
Successfully trained classifiers using clinician-labeled motion data.
Enabled direct upload of optimized FES parameters for stand-alone operation.
Facilitated immediate, real-time adjustment of FES protocols based on subject motion.

Conclusions:

The proposed FSM-based approach significantly simplifies FES programming.
This method allows for efficient, data-driven optimization of FES parameters.
The system offers a practical solution for clinicians to customize FES therapies.
This intuitive programming strategy has the potential to enhance FES application in clinical practice.