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.
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.
