Human-Machine Ritual: Synergic Performance through Real-Time Motion Recognition
This provides a replicable framework for integrating dance-literate machines into creative, educational, and live performance contexts, though it appears incremental as it builds on existing motion recognition and classification methods.
The researchers tackled the problem of enabling synergic human-machine performance by developing a lightweight, real-time motion recognition system using wearable IMU sensors and MiniRocket classification, achieving high accuracy with less than 50 ms latency.
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.