# Decomposing Juggling Skill into Sequencing, Prediction, and Accuracy: A Computational Model with Low-Gravity VR Training

## TL;DR

This study breaks down juggling into key components and uses VR to improve learning, showing how different skills contribute to overall performance.

## Contribution

The first computational decomposition of juggling into sequencing, prediction, and accuracy using a multimodal evaluation system.

## Key findings

- Sequencing is the dominant factor in early juggling skill acquisition.
- Prediction and accuracy become increasingly important as skill develops.
- Reduced gravity VR training enhances early-stage motor learning.

## Abstract

Juggling is a complex motor skill that requires multiple sub-skills and cannot be mastered without extensive practice. Although prior studies have quantified performance differences between novice and expert jugglers, none have attempted to quantitatively decompose these components or model their contribution to juggling performance. This longitudinal study presents a multimodal evaluation system that integrates computer vision, motion capture, and biosensing to quantify three key elements of juggling ability: Sequencing, Prediction, and Accuracy. Twenty beginners completed a 10-day, three-ball juggling experiment combining visuo-haptic virtual reality (VR) and real-world practice, with half training in reduced gravity, previously shown to enhance early-stage motor learning. The fitted Gamma-Log generalized linear model (GLM) indicated that Sequencing is the dominant factor of early skill acquisition, followed by Prediction and Accuracy. This study provides the first computational decomposition of juggling, demonstrates how multiple elements jointly contribute to performance, and results in a principled approach to characterizing motor learning in complex real-world tasks.