O-GEST: Overground gait events detector using b-spline-based geometric models for marker-based and markerless...

Area of Science:

• Biomechanics
• Biomedical Engineering
• Kinetics

Background:

• Accurate gait event detection is crucial for assessing normal and pathological gaits.
• Detecting gait events without force plates presents significant challenges.
• Existing methods often lack accuracy or require specialized equipment.

Purpose of the Study:

• To introduce O-GEST, an automatic algorithm for overground gait event detection using kinematic data.
• To validate O-GEST's accuracy and compare it with existing methods.
• To enable reliable gait analysis in force-plate-less environments.

Main Methods:

• Developed O-GEST algorithm integrating two geometric models (B-Spline-based) for foot landmark trajectory representation.
• Utilized gait-dependent thresholds and optimal coefficients for event detection and spatiotemporal parameter computation.
• Validated O-GEST using gait data from 390 subjects (healthy, hip osteoarthritis, stroke, Parkinson's disease, cerebral palsy).

Main Results:

• O-GEST achieved high accuracy in gait event detection: 13.5 ms for foot-strike and 12.6 ms for foot-off.
• Demonstrated significantly superior accuracy compared to common deep learning-based and kinematic-based methods.
• Showcased applicability across diverse pathologies and robustness to noisy trajectories.

Conclusions:

• O-GEST provides a reliable and accurate method for gait event detection without force plates.
• The algorithm is versatile, applicable to various pathologies and markerless setups.
• O-GEST enhances the feasibility of precise gait analysis in clinical and research settings lacking force plates.