Motion Training Data Pipeline Protocol

> **Version**: 1.0 > **Status**: DRAFT > **Scope**: End-to-end data architecture for motion capture, processing, and training This document defines the complete data pipeline from sensor capture to ML training corpus. The system is designed as an **infinite training grind** - a perpetual motion data collection environment that: 1. Captures motion from multiple sensor sources 2. Synchronizes with audio/phrase playback 3. Processes and normalizes data in real-time 4. Stores for immediate and future training 5. Supports upstream (generation) and downstream (analysis) tasks | Source | Data Type | Frequency | Landmarks | Priority | |--------|-----------|-----------|-----------|----------| | **MediaPipe (Webcam)** | Holistic pose | 30 fps | 33 pose + 21×2 hands + 468 face | Primary | | **Mocopi** | Full body IMU | 50 fps | 27 bones (Sony BVH) | Primary | | **Dual Phones** | Accelerometer + Gyro | 100 Hz | 2 devices (hands) | Secondary | | **Apple Watch** | Motion + Heart Rate | 50 Hz | Wrist orientation + HR | Secondary | | **Headphone Sensors** | Head orientation | 100 Hz | 3-axis rotation | Tertiary | | **LIDAR/Depth** | Point cloud | 30 fps | Sparse body points | Experimental | | Body Region | Primary | Fallback 1 | Fallback 2 | |-------------|---------|------------|------------| | Head | Headphones | MediaPipe | Mocopi | | Torso | Mocopi | MediaPipe | - | | Arms | Mocopi | MediaPipe | Phones | | Hands | MediaPipe | Phones | Mocopi | | Legs | Mocopi | MediaPipe | - | | Feet | Mocopi | MediaPipe | - |