Proposal: Using ARKit Body Tracking & LiDAR for Sign Language Education (Real-time Feedback)

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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.

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artefinalistajr OP

1d

Update: Refining the Architecture with LLMs (Gloss-to-Text)

I've been refining the concept to make development faster and less data-dependent. Instead of trying to solve "Continuous Sign Language Recognition" purely through computer vision (which is extremely hard), we can split the workload.

The Hybrid Pipeline Proposal:

Vision Layer (ARKit): Focus strictly on Isolated Sign Recognition. The CoreML model only needs to identify individual signs (Glosses) based on the skeleton data. It treats gestures as "Tokens".
- Input: Skeleton movement.
- Output: Raw tokens like [I], [WANT], [WATER], [PLEASE].
Logic Layer (LLM): We feed these raw tokens into an On-Device LLM (or API). Since LLMs excel at context and syntax, the model reconstructs the sentence based on the tokens.
- Input: [I] [WANT] [WATER] [PLEASE]
- Output: "I would like some water, please."

Why this is faster to build: We don't need a dataset of millions of complex sentences to train the Vision Model. We only need a dictionary of isolated signs. The "grammar" part is offloaded to the LLM, which is already solved technology. This drastically lowers the barrier for creating a functional prototype.

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