Automatic detection of human gait events: a simple but versatile 3D algorithm

Abstract

Abstract Background Detecting Foot Strike and Foot Off events in human gait, which is cyclic yet variable, consistently requires expert correction. This subjective correction can reduce spatiotemporal parameters, joint kinematic and kinetic accuracy, regardless of the gait event detection algorithm used from the literature. Recently developed methods have combined existing algorithms to better capture this gait variability, using Ground Reaction Forces. However, those methods do not fully account for intra-individual variability, particularly in the case of multiple and simultaneous gait patterns. Method We developed a deterministic algorithm called the Multi-Condition algorithm. This algorithm identifies the Foot Strike when the first of the foot markers loses its degrees of freedom and the Foot Off when the last of the foot markers regains its degrees of freedom. Results This algorithm was tested on 819 C3D gait files from 9 healthy individuals and 50 individuals with stroke, multiple sclerosis, spinal cord injury, cerebral palsy, polio, neuromuscular disease or amputation. The Multi-Condition algorithm detected both Foot Strike and Foot Off within a range of three frames, which was more accurate and precise than the inter-rater variability of expert correction. Detection of gait events required only a few seconds, regardless of the pathology or gait pattern, even when considering intra-individual variability. Conclusion Accurately identifying gait events is the first critical step in providing reliable gait analysis parameters for clinicians. The Multi-Condition algorithm aims to achieve deterministic consensus in the accurate and precise identification of gait events, regardless of the pathology or the gait pattern. To promote its adoption and ongoing testing, the Multi-Condition algorithm is available as an open-access resource. Ethical committee The study was approved by the University of Paris-Saclay Research Ethics Committee (No. CER-Paris-Saclay-2024-35) and was performed in accordance with the Declaration of Helsinki.

Keywords

• Biomechanical gait analysis
• Automatic gait event detection
• Optoelectronic motion capture
• Rehabilitation engineering
• Foot kinematics