EMVIZ (flow): An Artistic Tool for Visualising Movement Quality
Bibliographic record
Abstract
EMVIZ (flow) is an interactive artistic visualisation system that maps movement quality data to aesthetic visual representations. The goal of EMVIZ is to communicate complex movement information to an ‘everyday’ audience and support discernment of the experience of complex movement data. EMVIZ (flow) generates dynamic visual representations of human movement qualities derived from a framework of Laban Movement Analysis (LMA), a rigorous, analytical and embodied system for analysing human movement. Movement data is obtained from a real-time wearable sensor classifier supervised learning system that applies an LMA model to extract movement qualities from a moving body in the form of Laban Basic-Effort-Actions (BEA), This movement quality recognition system outputs a stream of Basic-Effort-Action vectors and EMVIZ (flow) maps this stream of data to an autonomous flocking agents system and colour palettes for creating visual representations of movement quality. EMVIZ (flow) was used in an improvised interactive dance performance at the Human Factors in Computing System (CHI) workshop 2011 and exhibited at a Simon Fraser University (SFU) Open House 2011 event. We describe an underlying model to capture and map movement quality to a visualisation system, a data mapping strategy, a generative algorithm, and an application used for visualising movement quality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".