Human-Machine Ritual: Synergic Performance through Real-Time Motion Recognition
Zhuodi Cai, Ziyu Xu, Juan Pampin

TL;DR
This paper presents a real-time, lightweight motion recognition system that enhances human-machine collaboration in dance and performance by combining wearable sensors, time-series classification, and multimedia control.
Contribution
It introduces a novel, human-centered approach that preserves expressive movement while enabling machine learning-based responsiveness in live performance settings.
Findings
Achieves high accuracy classification with less than 50 ms latency.
Supports integration of dance-literate machines into creative and educational contexts.
Provides a replicable framework for real-time motion recognition in performance.
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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Taxonomy
TopicsHuman Motion and Animation · Human Pose and Action Recognition · Diversity and Impact of Dance
