Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Performance of QR Code Detectors near Nyquist Limits.

Sensors (Basel, Switzerland)·2022
Same author

Detection and Classification of Artifact Distortions in Optical Motion Capture Sequences.

Sensors (Basel, Switzerland)·2022
Same author

Recreating the Motion Trajectory of a System of Articulated Rigid Bodies on the Basis of Incomplete Measurement Information and Unsupervised Learning.

Sensors (Basel, Switzerland)·2022
Same author

Evaluation of Keypoint Descriptors for Flight Simulator Cockpit Elements: WrightBroS Database.

Sensors (Basel, Switzerland)·2021
Same author

Gap Reconstruction in Optical Motion Capture Sequences Using Neural Networks.

Sensors (Basel, Switzerland)·2021
Same author

Optical motion capture dataset of selected techniques in beginner and advanced Kyokushin karate athletes.

Scientific data·2021

Related Experiment Video

Updated: Jul 8, 2026

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation
20:12

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation

Published on: October 8, 2011

31.0K

On the Noise Complexity in an Optical Motion Capture Facility.

Przemysław Skurowski1, Magdalena Pawlyta2

  • 1Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland. przemyslaw.skurowski@polsl.pl.

Sensors (Basel, Switzerland)
|October 17, 2019
PubMed
Summary

This study reveals that simple noise models are insufficient for optical motion capture. Using Allan variance, researchers identified multiple noise types, improving motion capture accuracy and calibration.

Keywords:
Allan varianceant colony optimizationevaluationmotion capturenoise colornoise modellingsimulated annealing

More Related Videos

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution
08:48

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Published on: September 5, 2012

12.3K
Bringing the Visible Universe into Focus with Robo-AO
10:35

Bringing the Visible Universe into Focus with Robo-AO

Published on: February 12, 2013

20.0K

Related Experiment Videos

Last Updated: Jul 8, 2026

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation
20:12

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation

Published on: October 8, 2011

31.0K
Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution
08:48

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Published on: September 5, 2012

12.3K
Bringing the Visible Universe into Focus with Robo-AO
10:35

Bringing the Visible Universe into Focus with Robo-AO

Published on: February 12, 2013

20.0K

Area of Science:

  • Measurement Science
  • Instrumentation
  • Signal Processing

Background:

  • Optical motion capture is a leading technology for motion acquisition.
  • Existing noise models often use a simple variance, which may be insufficient.
  • Accurate noise characterization is crucial for high-precision applications and calibration.

Purpose of the Study:

  • To demonstrate the insufficiency of simple noise models in optical motion capture.
  • To identify and quantify various noise types present in optical motion capture systems using Allan variance.
  • To analyze the impact of system parameters and failures on noise characteristics.

Main Methods:

  • Application of Allan variance to quantify different noise types in optical motion capture data.
  • Utilizing sophisticated metaheuristics for automated readout of noise coefficients.
  • Solving multidimensional regression problems for noise analysis.
  • Investigating the scaling of noise types with the number of cameras and the effect of camera failure.

Main Results:

  • Identified significant contributions from white noise and random walk, with minor contributions from blue noise and flicker; violet noise was absent.
  • Detected the presence of correlated noises and periodic distortions beyond classic noise types.
  • Observed how noise characteristics change with an increasing number of cameras.
  • Documented the influence of camera failure on overall system performance.

Conclusions:

  • A comprehensive noise model beyond simple variance is necessary for accurate optical motion capture.
  • Allan variance is an effective tool for detailed noise characterization in motion capture systems.
  • Understanding and quantifying diverse noise sources are critical for advancing optical motion capture applications, especially in calibration and high-fidelity recording.