Hi everyone, I’ve been analyzing the current state of Sign Language accessibility tools, and I noticed a significant gap in learning tools: we lack real-time feedback for students (e.g., "Is my hand position correct?"). Most current solutions rely on 2D video processing, which struggles with depth perception and occlusion (hand-over-hand or hand-over-face gestures), which are critical in Sign Language grammar. I'd like to propose/discuss an architecture leveraging the current LiDAR + Neural Engine capabilities found in iPhone devices to solve this. The Concept: Skeleton-based Normalization Instead of training ML models on raw video frames (which introduces noise from lighting, skin tone, and clothing), we could use ARKit's Body Tracking to abstract the input. Capture: Use ARKit/LiDAR to track the user's upper body and hand joints in 3D space. Data Normalization: Extract only the vector coordinates (X, Y, Z of joints). This creates a "clean" dataset, effectively normalizing the user regardless of physical appearance. Comparison: Feed these vectors into a CoreML model trained on "Reference Skeletons" (recorded by native signers). Feedback Loop: The app calculates the geometric distance between the user's pose and the reference pose to provide specific correction (e.g., "Raise your elbow 10 degrees"). Why this approach? Solves Occlusion: LiDAR handles depth much better than standard RGB cameras when hands cross the body. Privacy: We are processing coordinates, not video streams. Efficiency: Comparing vector sequences is computationally cheaper than video analysis, preserving battery life. Has anyone experimented with using ARKit Body Anchors specifically for comparing complex gesture sequences against a stored "correct" database? I believe this "Skeleton First" approach is the key to scalable Sign Language education apps. Looking forward to hearing your thoughts.