Feasibility of a Sensor-Based Gait Event Detection Algorithm for Triggering Functional Electrical Stimulation during...

Area of Science:

• Rehabilitation Engineering
• Biomedical Signal Processing
• Neuroscience

Background:

• Robot-assisted gait trainers and functional electrical stimulation (FES) are key technologies for gait disorder rehabilitation.
• Combining these technologies offers synergistic benefits, potentially overcoming individual limitations and improving therapeutic outcomes.
• Integrating FES with gait trainers requires precise gait event detection for effective stimulation timing.

Purpose of the Study:

• To develop and assess an algorithm for detecting gait events during robot-assisted gait training using inertial measurement units (IMUs).
• To evaluate the feasibility of triggering functional electrical stimulation (FES) based on IMU-detected gait events.
• To determine the accuracy and reliability of the proposed system for enhancing gait rehabilitation.

Main Methods:

• A custom algorithm was designed to analyze movement data from IMUs attached to participants during robot-assisted gait training.
• Gait events were detected based on the processed IMU data.
• The system was tested on a healthy adult using two different robot-assisted gait trainers (Lokomat and Lyra).

Main Results:

• The algorithm achieved high detection rates: 98.1% ± 5.2% for Lokomat and 94.1% ± 6.8% for Lyra.
• Low mean Type-1 errors were recorded: 0.3% ± 1.2% for Lokomat and 1.9% ± 4.3% for Lyra.
• The results demonstrate the system's effectiveness in accurately detecting gait events for potential FES triggering.

Conclusions:

• The developed system using IMUs for gait event detection is feasible and effective for robot-assisted gait training.
• This technology enables the integration of functional electrical stimulation with various robot-assisted gait trainers.
• The findings support further research into enhancing gait rehabilitation through combined robotic and FES interventions.