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

Théo Vancanneyt¹, Camille Le Moal¹, Maxence Blard¹

¹Movement Analysis Laboratory, Physiology and Functional Explorations Department, Raymond Poincaré Hospital, Paris Saclay University, AP-HP, 104 Bd. Raymond Poincaré, 92380, Garches, France.

Journal of Neuroengineering and Rehabilitation

|May 13, 2025

Summary

A new Multi-Condition algorithm accurately detects gait events like Foot Strike and Foot Off. This method improves precision over expert correction, even with varied gait patterns, aiding reliable clinical gait analysis.

Keywords:

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

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Area of Science:

Biomechanics
Gait Analysis
Rehabilitation Engineering

Background:

Accurate detection of gait events (Foot Strike, Foot Off) is crucial for reliable gait analysis but is often subjective and requires expert correction.
Existing algorithms struggle with intra-individual gait variability and multiple simultaneous gait patterns.
Ground Reaction Forces-based methods improve gait variability capture but don't fully address intra-individual differences.

Purpose of the Study:

To develop a deterministic algorithm for accurate and precise identification of gait events.
To overcome limitations of existing methods in handling gait variability and diverse pathologies.
To provide a reliable tool for clinical gait analysis.

Main Methods:

Developed a deterministic algorithm named the Multi-Condition algorithm.
Identified Foot Strike by detecting when the first foot marker loses degrees of freedom.
Identified Foot Off by detecting when the last foot marker regains degrees of freedom.

Main Results:

Tested on 819 C3D gait files from 9 healthy and 50 individuals with various neurological and motor impairments.
The Multi-Condition algorithm detected Foot Strike and Foot Off within a three-frame range, surpassing inter-rater expert variability.
Gait event detection was rapid (seconds) and robust across different pathologies and intra-individual gait variations.

Conclusions:

Accurate gait event identification is fundamental for reliable clinical gait analysis.
The Multi-Condition algorithm offers deterministic consensus for precise gait event identification across diverse pathologies and gait patterns.
The algorithm is available as an open-access resource to encourage adoption and further research.