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Updated: Apr 10, 2026

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software
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An open-source tool for automated human-level circling behavior detection.

O R Stanley1, A Swaminathan1, E Wojahn1

  • 1Department of Biomedical Engineering, Johns Hopkins University, 720 Rutland Ave, Traylor 504, Baltimore, MD, 21205-2109, USA.

Scientific Reports
|September 8, 2024
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Summary
This summary is machine-generated.

Researchers developed a new automated method to detect circling behavior in mice. This technique accurately identifies circling, matching human observer performance and aiding research in mouse models.

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Area of Science:

  • Neuroscience
  • Behavioral Science
  • Biomedical Engineering

Background:

  • Quantitatively linking animal behavior to biological mechanisms is essential in life sciences.
  • Automated analysis of postural data for specific behavior identification remains challenging.
  • Manual behavior coding is time-consuming and prone to inconsistency.

Purpose of the Study:

  • To develop an effective and automated technique for detecting circling behavior in mice.
  • To provide a quantitative, non-invasive tool for analyzing mouse models exhibiting circling.
  • To establish a method adaptable for detecting other complex behaviors.

Main Methods:

  • Utilized keypoint tracking data from videos of freely-exploring mice.
  • Developed a circling detection technique through simple postprocessing of keypoint data.
  • Validated the method using (Cib2-/-;Cib3-/-) mutant mice known to exhibit circling.

Main Results:

  • The developed technique achieved statistical parity with human observers in identifying circling occurrence times.
  • The method accurately distinguished between wild-type mice and mutant mice exhibiting circling.
  • The pipeline offers a user-friendly tool requiring no specialized software engineering skills.

Conclusions:

  • The proposed method provides a reliable and accessible tool for automated circling detection in mice.
  • The underlying principles are behavior-agnostic, suggesting broader applicability to other research-relevant behaviors.
  • This approach facilitates quantitative analysis of specific behaviors in animal models.