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

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.