Mathis D’haene
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Mathis D’haene has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Elodie Piche
Université Côte d’Azur, LAMHESS, Nice, France
1 shared publication - Frédéric Chorin
Laboratoire Motricité Humaine Éducation Sport Santé (LAMHESS), Université Côte d’Azur, 06100 Nice, France
1 shared publication - Olivier Guérin
Service de Médecine Gériatrique Aiguë, CHU de Nice, Hôpital de Cimiez, Nice, France
1 shared publication - Raphael Zory
LAMHESS, Nice, France
1 shared publication - Serge Sébastien Colson
Université Côte d’Azur, LAMHESS
1 shared publication
Latest Publications
- Open Access
Validation of a 3D Markerless Motion Capture Tool Using Multiple Pose and Depth Estimations for Quantitative Gait Analysis
Gait analysis is essential for evaluating walking patterns and identifying functional limitations. Traditional marker-based motion capture tools are costly, time-consuming, and require skilled operators. This study evaluated a 3D Marker-less Motion Capture (3D MMC) system using pose and depth estimations with the gold-standard Motion Capture (MOCAP) system for measuring hip and knee joint angles during gait at three speeds (0.7, 1.0, 1.3 m/s). Fifteen healthy participants performed gait tasks which were captured by both systems. The 3D MMC system demonstrated good accuracy (LCC > 0.96) and excellent inter-session reliability (RMSE < 3°). However, moderate-to-high accuracy with constant biases was observed during specific gait events, due to differences in sample rates and kinematic methods. Limitations include the use of only healthy participants and limited key points in the pose estimation model. The 3D MMC system shows potential as a reliable tool for gait analysis, offering enhanced usability for clinical and research applications.
Open AccessValidation of a 3D Markerless Motion Capture Tool Using Multiple Pose and Depth Estimations for Quantitative Gait Analysis
Gait analysis is essential for evaluating walking patterns and identifying functional limitations. Traditional marker-based motion capture tools are costly, time-consuming, and require skilled operators. This study evaluated a 3D Marker-less Motion Capture (3D MMC) system using pose and depth estimations with the gold-standard Motion Capture (MOCAP) system for measuring hip and knee joint angles during gait at three speeds (0.7, 1.0, 1.3 m/s). Fifteen healthy participants performed gait tasks which were captured by both systems. The 3D MMC system demonstrated good accuracy (LCC > 0.96) and excellent inter-session reliability (RMSE < 3°). However, moderate-to-high accuracy with constant biases was observed during specific gait events, due to differences in sample rates and kinematic methods. Limitations include the use of only healthy participants and limited key points in the pose estimation model. The 3D MMC system shows potential as a reliable tool for gait analysis, offering enhanced usability for clinical and research applications.