Motion Autocomplete Architecture

## Objective Predict a near-future physical intent (sub-second horizon) and execute environment preparation actions before movement completion. ## Runtime Pipeline 1. **Ingestion (`src/ingestion`)** - Simulated multimodal stream generator emitting a `MotionFrame` per timestep. - Modalities: IMU acceleration/gyroscope, pose reach vector/torso tilt/wrist height, local environment metadata. 2. **Fusion Prediction (`src/engine/predictor.py`)** - Sliding window over recent frames (`window_size`, default 8). - Computes aggregated features: vertical acceleration, turn rate, forward reach, wrist height, torso tilt, planar acceleration. - Scores intents and selects max-confidence class: - `lifting_object` - `turning_around` - `reaching_for_object` - `walking_towards_exit` - `idle` 3. **Context Preparation (`src/context/orchestrator.py`)** - Applies a minimum confidence gate. - Enforces per-intent cooldown to prevent repeated rapid triggers. - Emits structured actions (`target`, `operation`, `priority`, `reason`) for downstream systems. 4. **Presentation (`src/main.py`)** - Human-readable console trace or JSON events for integration. ## Data Contracts - `MotionFrame`: combines `MotionData`, `PoseData`, and `EnvironmentContext`. - `PredictedIntent`: includes `intent_label`, `confidence`, `horizon_ms`, and feature metadata. - `OrchestrationResult`: includes status (`prepared` / `suppressed_*`) and context actions. ## Configuration `config/default.json` - Predictor: `window_size`, `prediction_horizon_ms` - Orchestrator: `min_confidence`, `cooldown_seconds` ## Extension Points - Replace heuristic fusion scores with trained sequence model (LSTM/Transformer). - Feed live streams from Graph Kernel/Perception Mesh endpoints. - Persist predictions/actions to Supabase for online adaptation.