Abdominal's pinned hands used elbow -40 as the IK plane hint, drawing
the arms hyperextended (user-reported). Flipping the hint bends the
elbows the natural way while the hands stay on the handles. Same class
of fix for the milder cases: Arm Curl and Shoulder Press elbows and
Calfs knees clamp to -8, Side Plank's raised arm to the -70 ROM cap.
The whole library now passes render.py --strict with zero warnings,
making it a valid verification gate. Fixtures regenerated; 48 tests
green.
Claude-Session: https://claude.ai/code/session_01HJDQQDA9QdP8zByg43H5v3
The library's planar world-angle rig becomes a genuine 3D anatomical
model: skeleton.json holds bone-length profiles (real shoulder/pelvis
widths, feet, neutral/female/male) and per-joint ROM; motions pose
joints with anatomical angles (flexion/abduction/rotation from neutral
standing) under a per-exercise orthographic camera, resolved by
kinematics.py (3D FK, analytic two-bone IK with anatomical write-back)
and validated against physiological ranges. All 20 sagittal motions
were migrated by planar decomposition with 0.00 px golden parity against
the old renderer — relabeled to true anatomy, since shading is now
near-dark/far-light by camera depth rather than by limb suffix — and
the face-on machines are re-authored honestly: Abductor/Adductor with
real hip abduction (the foreshortened "frontal" profile is retired) and
Rotary with genuine spine axial rotation. Figures gain articulated
feet; profiles swap without touching a single motion script; --orbit
sweeps the camera 360° while a motion loops.
The in-app SwiftUI renderer (iOS + watch) is ported to the same model
and consumes the exported motions verbatim; figure-fixtures.json pins
its geometry to the Python pipeline within 0.5 px across every
exercise, key frame, tween, and orbit sample. Also makes the watch
bridge logger nonisolated for the newer SDK's stricter isolation
checking.
Claude-Session: https://claude.ai/code/session_01LEoff8bXGBS83tK1c55Mf7
Exercise Library/ holds per-exercise reference docs (setup, cues,
mistakes, progressions) with SVG visuals and a Python-rendered motion
pipeline; Workouts/ExerciseFigure renders the bundled *.motion.json
rigs as animated stick figures on the exercise screen. Exercises gain
a warm-up/main-circuit category, timed exercises display hold time via
planSummary, and a completed exercise reopens to a check screen instead
of its timers.
rzen