Soutenance de thèse - Mehran Hatamzadeh - 04 novembre 2024

Motion analysis by video for gait evaluation with innovative technology

Jury Members:

• Raphael Dumas, LBMC, Université Gustave Eiffel and Université Claude Bernard Lyon 1, France
• Laetitia Fradet, Institut Pprime, Université de Poitiers, France
• Francois Bremond, STARS Team, Inria Center of Université Côte d'Azur, France
• Stefanie Wuhrer, MORPHEO Team, Inria Center of Université Grenoble Alpes, France

• Raphael Zory, LAMHESS, Université Côte d’Azur, France - Director
• Laurent Busé, AROMATH Team, Inria Center of Université Côte d’Azur, France - Co-director

• Katia Turcot, CIRRIS, Université Laval, Canada - Co-surpervisor

Invited Member:

• Pierre Alliez, TITANE Team, Inria Center of Université Côte d’Azur, France

Abstract:

The emergence of depth cameras (RGB-D) and the development of human pose estimation algorithms have paved the way to develop markerless motion analysis systems. Despite the advancements in recent years, some challenges still remain when employing them for gait analysis, as for instance noise in the generated 3D scene and noise of the estimated poses. Mixed with each other, these lead markerless gait analysis systems to demonstrate lower accuracy in the measured biomechanical parameters compared to the gold-standard systems.

In this dissertation, the inaccuracy problem of markerless gait analysis systems has been tackled by constructing geometric models with the integration of biomechanics in them. Three interconnected studies have been done to improve the accuracy of gait events detection, spatiotemporal parameters, and kinematic measurements. Firstly, we developed a Bezier curve-based geometric model that can imitate the horizontal trajectory of foot landmarks in the gait pattern of healthy subjects. To obtain landmarks’ trajectories during walking, a markerless setup was established based on an RGB-D camera (Microsoft Azure Kinect) and an artificial intelligence-based (AI) human pose estimator (OpenPose). Validation of spatiotemporal parameters computed from this model, compared to the OptoGait system, showed that it yields good to excellent absolute statistical agreement (0.86 ≤ Rc ≤ 0.99), and revealed its potential in pattern-aware denoising of the trajectory of foot landmarks of healthy subjects during walking. Secondly, building upon the above-mentioned foundation, the concept has been further expanded to adapt to both normal and pathological gaits. Hence, we developed two geometric models that encompass various gait patterns and released O-GEST, an automatic and publicly available algorithm for overground gait events detection in force plate-less environments (both marker-based and markerless). O-GEST employs B-Spline-based geometric models to represent the horizontal trajectory of foot landmarks. It leverages gait-dependent thresholds along with optimal coefficients to detect events and compute spatiotemporal parameters. O-GEST was validated against force plates in terms of the timing of the events on various pathologies including subjects with unilateral hip osteoarthritis, stroke survivors, individuals diagnosed with Parkinson’s disease, and children with cerebral palsy. Its validation shows that O-GEST detects 95% of the gait events with an absolute error of less than 20-30 ms in the above-mentioned cohorts. Thirdly, we developed a lower-limb geometric model and pipeline of an algorithm to improve kinematics accuracy in markerless gait analysis setups. The developed approach refines the 3D lower-limb skeletons obtained by AI-based pose estimation algorithms in a subject-specific geometric manner, preserves skeleton links’ length, benefits from gait phases information that adds biomechanical awareness to the algorithm, and utilizes an embedded trajectory smoothing. Its validation against a marker-based motion analysis system (OptiTrack) revealed its ability to reduce up to 43.5% of the error and improve kinematic curves’ similarity to the gold-standard ones.

Developed algorithms and obtained results in this dissertation suggest that constructing biomechanically-aware geometric models and employing them in markerless gait analysis could lead to enhanced accuracy and reliability of the analysis. Such algorithms can also help in increasing the portability of the system, such that fewer cameras are required which delivers comparable measurement accuracy compared to multi-camera approaches.
 

Keywords: 

Markerless gait analysis, RGB-D camera, Geometric modeling, Improved kinematics, Spatiotemporal calculation, Lower-limb model.