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Related Experiment Video

Updated: Sep 21, 2025

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Published on: July 20, 2017

Model Segmentation in Single Particle Tracking.

Boris I Godoy¹, Nicholas A Vickers², Sean B Andersson^1,2

¹Department of Mechanical Engineering, Boston University, MA 02215, USA.

Ifac-Papersonline

|June 1, 2022

Summary

This summary is machine-generated.

This study compares two change detection methods, likelihood ratio test (LRT) and Kullback-Leibler divergence (KLD), for identifying motion changes in single particle tracking (SPT) data. These techniques effectively segment SPT data to analyze particle dynamics.

Keywords:

Estimation Identification and Signal Processing Modelling Stochastic Systems

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Area of Science:

Biophysics
Physical Chemistry
Data Analysis

Background:

Single Particle Tracking (SPT) generates complex trajectory data.
Identifying changes in particle motion is crucial for understanding dynamic processes.
Existing methods may require refinement for accurate motion model segmentation.

Purpose of the Study:

To implement and compare two change detection techniques for analyzing Single Particle Tracking (SPT) data.
To determine time points where particle motion dynamics change.
To enable accurate segmentation of SPT data for parameter estimation.

Main Methods:

Comparison of Likelihood Ratio Test (LRT) and Kullback-Leibler Divergence (KLD) for change detection.
Application to time-varying systems with step-like parameter changes.
Validation using experimental SPT data acquired under controlled microscopy conditions.

Main Results:

Both LRT and KLD were successfully implemented for change point detection in SPT data.
The study validates the effectiveness of these statistical methods in identifying transitions in particle motion.
Experimental data confirmed the utility of the chosen techniques for segmenting complex trajectories.

Conclusions:

Likelihood Ratio Test and Kullback-Leibler Divergence are effective tools for change detection in SPT.
Accurate segmentation of SPT data using these methods facilitates improved dynamic model parameter estimation.
The findings support the application of these techniques in biophysical and chemical research involving particle dynamics.