Motion Autocomplete - System Capabilities Report

Motion Autocomplete is a sophisticated AI system that predicts physical movements and prepares context before actions occur. The system has evolved through 8 generations, with the current implementation featuring: - ✅ **Kinetic Chain Detection** - Fully implemented via precursor detection - ✅ **Temporal Echo Patterns** - Markov transitions + time-of-day patterns - ✅ **Bio-Sync Features** - Circadian rhythm, HRV, respiratory coupling (newly added) - ✅ **Smart Home Integration** - Zone-based device orchestration - ✅ **Voice Override** - Natural language control The kinetic chain system is implemented through the **Precursor Detector** (`src/intent/precursor-detector.ts`), which detects micro-movements that precede major actions. The body broadcasts intent before conscious action. The system detects these precursors with **500-2000ms of lead time**: | Precursor Type | Detection Method | Lead Time | |---------------|------------------|-----------| | `weight-shift` | Accelerometer center-of-gravity | 800-1500ms | | `gaze-redirect` | Eye tracking / head turn | 500-1000ms | | `hand-preparation` | Gyroscope hand positioning | 300-800ms | | `screen-disengage` | Keyboard/mouse activity drop | 1000-2000ms | | `postural-adjustment` | Subtle position changes | 500-1000ms | | `breathing-change` | Heart rate variability | 1500-3000ms | | `grip-release` | Mouse/object release | 300-600ms | | `muscle-tension` | Pre-movement tension buildup | 400-800ms |