Semi-supervised learning for time-series segmentation in equine gait event detection [r-libre/4183]

Gérard, Mahaut, Dubois, Guillaume, Hanne-Poujade, Sandrine, Hebert, Camille, Chateau, Henry et Mezghani, Neila (2026). Semi-supervised learning for time-series segmentation in equine gait event detection. IEEE Access, 14, 68815 - 68829. 10.1109/ACCESS.2026.3688918

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Catégorie de document :	Articles de revues
Évaluation par un comité de lecture :	Oui
Étape de publication :	Publié
Résumé :	Locomotor injuries in horses impair both welfare and performance. Traditional visual assessments of equine locomotion by veterinarians are inherently subjective. To provide objective evaluation, systems using Inertial Measurement Units (IMUs) have been developed, enabling quantitative analysis by calculating trunk symmetry indices (head, withers, pelvis) during trot. Stance temporal parameters provide valuable information for locomotion analysis, but their accurate estimation requires precise detection of stance-related events. Gait events are typically identified using signal processing techniques applied to data from limb-mounted IMUs, often placed on the cannon bones. Despite their potential, these sensors are not well tolerated by all horses and the associated signal processing methods often fail to generalize across diverse locomotion patterns. A large amount of data is gathered by veterinarians in uncontrolled environments, opening the door for deep learning methods, but the annotation process needed for supervised learning remains time-consuming and costly. The aim of this study was to segment horse stances using trunk-mounted IMU data and to introduce two novel semi-supervised time-series segmentation methods, specifically tailored for contexts with limited strongly labeled data and abundant weakly labeled data. The best performing pipeline achieved errors of −0.1±1.6 % of the stride duration for foot-on and 0.2±2.6 % for heel-off events of the right forelimb, and 0.2±1.5 % for foot-on and −0.4±1.4 % for heel-off events of the right hindlimb. Beyond improving stance segmentation in real clinical settings, this work explores two semi-supervised methods for time-series segmentation.
Adresse de la version officielle :	https://ieeexplore.ieee.org/abstract/document/1149...
Déposant:	Ayena, Johannes
Responsable :	Johannes Ayena
Dépôt :	11 aout 2026 18:17
Dernière modification :	11 aout 2026 18:17

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