My work currently lives in the smallest but least-served part of this map: spine-aware understanding from external sensing, especially RGB and multiview motion, with a stronger emphasis on vertebral structure and biomechanics than conventional pose estimation.
Where my work fits
SpineTrack
Paper + dataset · Available now
Moves the field from generic human joints toward spine-centric 2D landmarks in natural RGB images.
SIMSPINE
Paper + dataset + benchmark · Available now
Extends the problem into multiview 3D supervision, vertebral kinematics, and biomechanics-aware benchmarking.
SpinePose
Open-source inference toolkit · Available now
Turns released models into a usable inference API, so the work is not locked inside papers.
PoseAdapt
Adaptation benchmark · Adjacent research line
Targets continual adaptation and efficient optimization once pose models leave one fixed benchmark and meet changing domains.
What still needs work
Static anatomy is easier to benchmark than motion
CT and MRI already support shared segmentation challenges, but vertebral motion, rotations, and full-body spine biomechanics still lack common public evaluation.
Clinical utility and open reproducibility diverge
Commercial systems like EOS and DIERS are important in practice, yet their maturity does not translate into open datasets, code, or challenge infrastructure.
Biomechanics validation is still exceptional
Many methods optimize localization or segmentation metrics without checking whether the predicted posture or motion remains anatomically plausible.
Cross-modal fusion is underdeveloped
The field still treats radiographs, MRI, surface scans, and RGB motion as separate silos instead of complementary evidence streams.
Deployment quality is lagging behind publication quality
Even when methods are strong on paper, reusable inference interfaces, benchmark harnesses, and failure-analysis tools are usually missing.
Have a benchmark or spine-motion resource that belongs in this guide? Get in touch.