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Related Experiment Video

Updated: Aug 28, 2025

Preparation and 3D Tracking of Catalytic Swimming Devices

Preparation and 3D Tracking of Catalytic Swimming Devices

Published on: July 1, 2016

Tracking Janus microswimmers in 3D with machine learning.

Maximilian Robert Bailey¹, Fabio Grillo¹, Lucio Isa¹

¹Laboratory for Soft Materials and Interfaces, Department of Materials, ETH Zürich, Vladimir-Prelog-Weg 5, 8093 Zürich, Switzerland. maximilian.bailey@mat.ethz.ch.

|September 15, 2022

Summary

This summary is machine-generated.

Machine learning now enables 3D tracking of Janus microswimmers using standard microscopy. This approach, utilizing Extremely Randomised Decision Trees, overcomes limitations of 2D analysis for active matter systems.

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Area of Science:

Active matter physics
Microscopy and imaging
Machine learning applications

Background:

Characterizing artificial active matter motion is crucial for advancements.
Standard wide-field microscopy is limited to 2D motion analysis.
Real-world applications necessitate understanding 3D navigation.

Purpose of the Study:

To develop a Machine Learning (ML) approach for 3D tracking of Janus microswimmers.
To utilize Z-stacks as labeled training data for ML models.
To identify the most effective ML algorithm for this tracking task.

Main Methods:

Implementation of various ML algorithms using accessible software.
Training ML models with Z-stack data of Janus microswimmers.
Evaluation of tracking performance over a 40 μm volume.

Main Results:

An ensemble Decision Tree-based model (Extremely Randomised Decision Trees) demonstrated superior performance.
Successful localization of Janus particles with significant optical asymmetry.
Demonstrated capability to track particles in 3D using standard wide-field microscopy images.

Conclusions:

ML algorithms, particularly Extremely Randomised Decision Trees, offer a powerful tool for 3D active matter tracking.
This method bypasses the need for specialized equipment like digital holographic microscopy.
ML is expected to become increasingly vital for addressing challenges in active matter research.