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

• URL: http://arxiv.org/abs/2511.02351v1
• Date: Tue, 04 Nov 2025 08:15:25 GMT
• Title: Human-Machine Ritual: Synergic Performance through Real-Time Motion Recognition
• Authors: Zhuodi Cai, Ziyu Xu, Juan Pampin,
• Abstract summary: We introduce a real-time motion recognition system that enables synergic human-machine performance through wearable IMU sensor data.<n>By mapping dancer-specific movement to sound through somatic memory and association, we propose an alternative approach to human-machine collaboration.
• Score: 2.064612766965483
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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