Project Details
Description
3D Human pose estimation (HPE) is a computer vision task that
estimates the configuration of human body parts in the 3D space.
HPE is the main technology behind markerless motion capture,
which is usually preferred to a marker based one as it is more
portable, less expensive, and easier to set up. Nevertheless, its
accuracy is not adequate enough to be used in clinical applications
such as gait analysis, which therefore often remains restricted to
specialized clinics equipped with the gold standard marker-based
systems.
Thus far, HPE from monocular images and gait parameters
extraction are being treated as two separate problems; the former is
studied in the computer vision domain using deep learning
techniques while the latter in biomechanics. Instead, we define and
train both problems in the same deep learning framework to reach
higher accuracy in both tasks.
We have the ambition to run inference directly and in real time on
augmented reality glasses. The developed system will therefore be
extremely portable and easy to use and will allow the clinicians to
intuitively visualize gait parameters through holograms mixed in the
real world. Moreover, additional clinical parameters such as the
loads acting inside the knee are, so far, calculated offline through
complex modelling simulation. Instead, we propose a completely
data driven approach to achieve real time performances
estimates the configuration of human body parts in the 3D space.
HPE is the main technology behind markerless motion capture,
which is usually preferred to a marker based one as it is more
portable, less expensive, and easier to set up. Nevertheless, its
accuracy is not adequate enough to be used in clinical applications
such as gait analysis, which therefore often remains restricted to
specialized clinics equipped with the gold standard marker-based
systems.
Thus far, HPE from monocular images and gait parameters
extraction are being treated as two separate problems; the former is
studied in the computer vision domain using deep learning
techniques while the latter in biomechanics. Instead, we define and
train both problems in the same deep learning framework to reach
higher accuracy in both tasks.
We have the ambition to run inference directly and in real time on
augmented reality glasses. The developed system will therefore be
extremely portable and easy to use and will allow the clinicians to
intuitively visualize gait parameters through holograms mixed in the
real world. Moreover, additional clinical parameters such as the
loads acting inside the knee are, so far, calculated offline through
complex modelling simulation. Instead, we propose a completely
data driven approach to achieve real time performances
Acronym | FWOSB139 |
---|---|
Status | Active |
Effective start/end date | 1/11/22 → 31/10/26 |
Keywords
- Markerless Motion Capture
- Augmented Reality
- Gait Analysis
Flemish discipline codes in use since 2023
- Data visualisation and imaging
- Computational biomodelling and machine learning
- Rehabilitation engineering
- System and whole body biomechanics
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