DanZens: A Toolkit for Sensing, Labeling and Visualizing Dance Movements

Wearable technology offers new opportunities for analyzing complex movements like dance, where precision, coordination, and feedback are key. In this demo, we present DanZens, a novel toolkit for real-time motion analysis in dance, leveraging wearable sensors to provide accessible and actionable feedback. Combining DanceTag to capture and annotate movements with DanceVis to visualize performance differences, DanZens uses Sony Mocopi sensors to analyze motion and generate intuitive heat maps through Dynamic Time Warping (DTW). This system enables precise, cost-effective comparisons between two people doing dance-related movements, offering personalized feedback and eliminating the need for expensive biomechanical labs. Designed to advance pervasive computing in interactive and creative domains, DanZens demonstrates the potential of wearable technology to transform movement analysis and learning experiences.

Authors

Institutions

Publication Details

Published
2025-03-17
DOI
https://doi.org/10.1109/percomworkshops65533.2025.00125
Primary Topic
Diversity and Impact of Dance
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

DanZens: A Toolkit for Sensing, Labeling and Visualizing Dance Movements

Héctor M. Camarillo-Abad, Franceli L. Cibrian, María Concepción Valdez Gastelum, Yanelly Mego
Diversity and Impact of Dance
article

DanZens: A Toolkit for Sensing, Labeling and Visualizing Dance Movements

Héctor M. Camarillo-Abad, Franceli L. Cibrian, María Concepción Valdez Gastelum, Yanelly Mego
article en

Abstract

Wearable technology offers new opportunities for analyzing complex movements like dance, where precision, coordination, and feedback are key. In this demo, we present DanZens, a novel toolkit for real-time motion analysis in dance, leveraging wearable sensors to provide accessible and actionable feedback. Combining DanceTag to capture and annotate movements with DanceVis to visualize performance differences, DanZens uses Sony Mocopi sensors to analyze motion and generate intuitive heat maps through Dynamic Time Warping (DTW). This system enables precise, cost-effective comparisons between two people doing dance-related movements, offering personalized feedback and eliminating the need for expensive biomechanical labs. Designed to advance pervasive computing in interactive and creative domains, DanZens demonstrates the potential of wearable technology to transform movement analysis and learning experiences.

Chapman University (US), Centro de Investigación Científica y de Educación Superior de Ensenada (MX), Occidental College (US)
Reduced inequalities
Openalex Percentile: Top 86%
Diversity and Impact of Dance
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.