Human-Machine Ritual: Synergic Performance through Real-Time Motion Recognition

📝 Original Info

• Title: Human-Machine Ritual: Synergic Performance through Real-Time Motion Recognition
• ArXiv ID: 2511.02351
• Date: 2025-11-04
• Authors: 정보 없음 (논문에 저자 정보가 제공되지 않았습니다.)

📝 Abstract

We introduce a lightweight, real-time motion recognition system that enables synergic human-machine performance through wearable IMU sensor data, MiniRocket time-series classification, and responsive multimedia control. By mapping dancer-specific movement to sound through somatic memory and association, we propose an alternative approach to human-machine collaboration, one that preserves the expressive depth of the performing body while leveraging machine learning for attentive observation and responsiveness. We demonstrate that this human-centered design reliably supports high accuracy classification (<50 ms latency), offering a replicable framework to integrate dance-literate machines into creative, educational, and live performance contexts.

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