Evidence map›Paper›PMID 40361153›Full record

ArticleJournal of neuroengineering and rehabilitation2025

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

Théo Vancanneyt, Camille Le Moal, Maxence Blard, Juliette Lenoir, Nicolas Roche, Céline Bonnyaud, Fabien Dubois

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of neuroengineering and rehabilitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Théo VancanneytMovement Analysis Laboratory, Physiology and Functional Explorations Department, Raymond Poincaré Hospital, Paris Saclay University, AP-HP, 104 Bd. Raymond Poincaré, 92380, Garches, France.
Camille Le MoalMovement Analysis Laboratory, Physiology and Functional Explorations Department, Raymond Poincaré Hospital, Paris Saclay University, AP-HP, 104 Bd. Raymond Poincaré, 92380, Garches, France.
Maxence BlardMovement Analysis Laboratory, Physiology and Functional Explorations Department, Raymond Poincaré Hospital, Paris Saclay University, AP-HP, 104 Bd. Raymond Poincaré, 92380, Garches, France.
Juliette LenoirERPHAN Research Unit 20201, Paris Saclay University, UVSQ, 78000, Versailles, France.
Nicolas RocheMovement Analysis Laboratory, Physiology and Functional Explorations Department, Raymond Poincaré Hospital, Paris Saclay University, AP-HP, 104 Bd. Raymond Poincaré, 92380, Garches, France.
Céline BonnyaudMovement Analysis Laboratory, Physiology and Functional Explorations Department, Raymond Poincaré Hospital, Paris Saclay University, AP-HP, 104 Bd. Raymond Poincaré, 92380, Garches, France.
Fabien DuboisMovement Analysis Laboratory, Physiology and Functional Explorations Department, Raymond Poincaré Hospital, Paris Saclay University, AP-HP, 104 Bd. Raymond Poincaré, 92380, Garches, France. fabien.dubois@aphp.fr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDetecting 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.

methodWe 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.

resultsThis 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.

conclusionAccurately 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.

Indexed as

AlgorithmsGaitGait AnalysisAdultBiomechanical PhenomenaFemaleFootHumansMaleMiddle AgedYoung AdultAutomatic gait event detectionBiomechanical gait analysisFoot kinematicsOptoelectronic motion captureRehabilitation engineering

Identifiers

PMID40361153
PMCPMC12077026

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.