Comparative Latency Analysis of Optical and Inertial Motion Capture Systems for Gestural Analysis and Musical Performance
Bibliographic record
Abstract
Wireless sensor-based technologies are becoming increasingly accessible and widely explored in interactive musical performance due to their ubiquity and low-cost, which brings the necessity of understanding the capabilities and limitations of these sensors.This is usually approached by using a reference system, such as an optical motion capture system, to assess the signals' properties.However, this process raises the issue of synchronizing the signal and the reference data streams, as each sensor is subject to different latency, time drift, reference clocks and initialization timings.This paper presents an empirical quantification of the latency communication stages in a setup consisting of a Qualisys optical motion capture (mocap) system and a wireless microcontroller-based sensor device.We performed event-to-end tests on the critical components of the hybrid setup to determine the synchronization suitability.Overall, further synchronization is viable because of the near individual average latencies of around 25ms for both the mocap system and the wireless sensor interface.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".