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Related Experiment Video

Updated: Nov 27, 2025

Three-Dimensional Finger Motion Tracking during Needling: A Solution for the Kinematic Analysis of Acupuncture Manipulation

Three-Dimensional Finger Motion Tracking during Needling: A Solution for the Kinematic Analysis of Acupuncture Manipulation

Published on: October 28, 2021

WhiskEras: A New Algorithm for Accurate Whisker Tracking.

Jan-Harm L F Betting¹, Vincenzo Romano¹, Zaid Al-Ars²

¹Department of Neuroscience, Erasmus MC, Rotterdam, Netherlands.

Frontiers in Cellular Neuroscience

|December 7, 2020

Summary

This summary is machine-generated.

We developed WhiskEras, a new algorithm for tracking rodent whisker movements in high-speed videos. This tool accurately identifies individual whiskers, improving studies of sensorimotor integration.

Keywords:

Purkinje cell algorithm cerebellum machine learning mouse object tracking sensorimotor integration whiskers

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Last Updated: Nov 27, 2025

Three-Dimensional Finger Motion Tracking during Needling: A Solution for the Kinematic Analysis of Acupuncture Manipulation

Three-Dimensional Finger Motion Tracking during Needling: A Solution for the Kinematic Analysis of Acupuncture Manipulation

Published on: October 28, 2021

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

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Published on: November 21, 2019

Whisker-signaled Eyeblink Classical Conditioning in Head-fixed Mice

Whisker-signaled Eyeblink Classical Conditioning in Head-fixed Mice

Published on: March 30, 2016

Area of Science:

Neuroscience
Biophysics
Computer Vision

Background:

Rodents use active touch with facial whiskers for environmental exploration.
Tracking individual whiskers in high-speed videos is challenging due to rapid movements and whisker crossings.

Purpose of the Study:

To introduce WhiskEras, a novel algorithm for tracking whisker movements in untrimmed mice.
To overcome limitations of existing whisker-tracking methods.

Main Methods:

WhiskEras employs a pipeline of image-processing steps: sub-pixel centerline detection, whisker clustering, parameterization, and temporal tracking.
The algorithm does not require labeled data.

Main Results:

WhiskEras detected more whiskers per frame compared to the Biotact Whisker Tracking Tool.
The algorithm achieved a higher signal-to-noise ratio than Janelia Whisk.
Improved whisker tracking enhanced the correlation between whisker movements and cerebellar Purkinje cell activity.

Conclusions:

WhiskEras significantly improves the accuracy of whisker tracking in untrimmed mice.
The algorithm facilitates research on sensorimotor integration.