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Efficient FES triggering applying Kalman filter during sensory supported treadmill walking.

I Cikajlo¹, Z Matjacić, T Bajd

¹Institute for Rehabilitation Republic of Slovenia, Linhartova 51, 1000 Ljubljana, Slovenia. imre.cikajlo@mail.ir-rs.si

Journal of Medical Engineering & Technology

|February 26, 2008

Summary

This study presents a novel algorithm for functional electrical stimulation (FES) gait re-education in spinal cord injury patients. The system uses sensor data and a Kalman filter for precise leg movement analysis, improving FES control.

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Area of Science:

Biomedical Engineering
Neurorehabilitation
Robotics

Background:

Incomplete spinal cord injury (SCI) often leads to impaired gait, necessitating advanced rehabilitation strategies.
Functional electrical stimulation (FES) offers motor augmentation but requires precise triggering for effective gait re-education.
Existing sensory systems may lack the accuracy needed for dynamic movement analysis in FES applications.

Purpose of the Study:

To develop and validate an algorithm for efficient FES triggering in gait re-education for individuals with incomplete SCI.
To integrate sensory data with a Kalman filter for accurate shank angle estimation during gait.
To enhance the reliability of FES control through advanced kinematic data assessment.

Main Methods:

A novel sensory system with a dual-axial accelerometer and gyroscope was developed and placed on the paretic leg shank.
A Kalman filter-based algorithm was implemented for precise shank angle estimation, correcting measurement errors.
The algorithm integrated kinematic data for efficient and reliable FES triggering, tested on neurologically intact individuals and during FES-assisted treadmill training.

Main Results:

The Kalman filter algorithm accurately estimated shank angle during dynamic movements, outperforming traditional inclinometers.
Sensory integration provided sufficient data for efficient control of FES, demonstrating its potential in gait rehabilitation.
Preliminary tests showed the system's efficacy in assessing gait kinematics for improved FES triggering.

Conclusions:

The proposed sensory integration and Kalman filter algorithm effectively address data assessment challenges in dynamic movements for FES gait re-education.
This approach enhances the precision and reliability of FES control, paving the way for improved rehabilitation outcomes in SCI patients.
The system demonstrates significant potential for advancing FES-based therapies for neurological gait impairments.