Computational Choreography: Deterministic Motion-to-Audio Synthesis via Geometric Anticipation Signals

We present Computational Choreography, a deterministic pipeline that transforms heterogeneous sensor input -- phone accelerometer, smartwatch heart rate, full-body IMU skeleton -- into real-time audio synthesis through geometric anticipation signals. The system guarantees deterministic replay: identical sensor input always produces identical audio output. The key innovation is the Anticipation Kernel, which computes seven geometric scalars (commitment, uncertainty, transition pressure, recovery margin, phase stiffness, novelty, stability) from fused sensor data, creating a continuous phase space that drives audio synthesis parameters. Unlike ad hoc motion-to-audio mappings that bind a single sensor axis to a single audio parameter, our approach provides a principled geometric foundation where the anticipation scalars capture the *intent* of movement before the movement completes. The full implementation comprises 334 Rust source files across the motion and audio layers, all verified to compile on Rust 1.92.0 stable, with five genre-specific synthesizer kits (House, Techno, Jazz, Electro, Ambient), Ableton Link beat synchronization, a Strudel.js live coding bridge, and companion iOS applications for sensor capture. Cross-domain validation demonstrates that the same anticipation scalars, applied unchanged to conversational turn-taking data, predict topic convergence at 71.8% accuracy (z = 2.72, p < 0.007), confirming that the geometric framework captures genuine trajectory dynamics rather than motion-specific artifacts. We describe the system architecture, report build verification and cross-domain results, and present designed evaluation protocols for determinism, latency, and musical quality assessment that await physical sensor data collection. **Keywords:** motion-to-audio, anticipation, sensor fusion, live coding, deterministic replay, Ableton Link, body-as-instrument, Strudel.js, cross-domain generalization