Evidence map›Paper›PMID 34968626›Full record

ArticleJournal of neuroscience methods2022

Wide-angle, monocular head tracking using passive markers.

Balazs P Vagvolgyi, Ravikrishnan P Jayakumar, Manu S Madhav, James J Knierim, Noah J Cowan

Open access · greenAbstract read
In one paragraph

Article in Journal of neuroscience methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
10.4field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 14 citations in OpenAlex.

  1. Vestibular Function Drives Gaze Stability in Locomoting Macaques.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026
    Article
  2. Acceleration and Velocity Dissociate Temporal Phases of Postural Control in Rhesus Macaques.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026
    Article
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  11. Naturalistic neuroscience and virtual reality.Frontiers in systems neuroscience · 2022
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 2 countries.

Balazs P VagvolgyiLaboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, USA. Electronic address: balazs@jhu.edu.
Ravikrishnan P JayakumarMind/Brain Institute, Johns Hopkins University, Baltimore, USA; Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, USA; Mechanical Engineering Department, Johns Hopkins University, Baltimore, MD, USA.
Manu S MadhavMind/Brain Institute, Johns Hopkins University, Baltimore, USA; Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, USA; Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, USA; School of Biomedical Engineering, Djawad Mowafaghian Centre for Brain Health, University of British Columbia, BC, Canada.
James J KnierimMind/Brain Institute, Johns Hopkins University, Baltimore, USA; Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, USA.
Noah J CowanLaboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, USA; Mechanical Engineering Department, Johns Hopkins University, Baltimore, MD, USA.
Johns Hopkins University · USAllen Institute for Brain Science · USDiscovery Institute · US

Funding

A Control Theoretic Approach to Addressing Hippocampal FunctionR01NS102537 · NINDS · JOHNS HOPKINS UNIVERSITY · PI Noah John Cowan, JAMES J KNIERIM · 2017 to 2026
$3.6M
CRCNS: Dynamics of Gain Recalibration in the Hippocampal-Entorhinal Path Integration SystemR01MH118926 · NIMH · JOHNS HOPKINS UNIVERSITY · PI COWAN, NOAH JOHN, HEDRICK, KATHRYN · 2018 to 2022
$1.8M
NIMH NIH HHS R01 MH118926NINDS NIH HHS R01 NS102537
6 · The paper itself

Abstract

backgroundCamera images can encode large amounts of visual information of an animal and its environment, enabling high fidelity 3D reconstruction of the animal and its environment using computer vision methods. Most systems, both markerless (e.g. deep learning based) and marker-based, require multiple cameras to track features across multiple points of view to enable such 3D reconstruction. However, such systems can be expensive and are challenging to set up in small animal research apparatuses. NEW

methodsWe present an open-source, marker-based system for tracking the head of a rodent for behavioral research that requires only a single camera with a potentially wide field of view. The system features a lightweight visual target and computer vision algorithms that together enable high-accuracy tracking of the six-degree-of-freedom position and orientation of the animal's head. The system, which only requires a single camera positioned above the behavioral arena, robustly reconstructs the pose over a wide range of head angles (360° in yaw, and approximately ± 120° in roll and pitch).

resultsExperiments with live animals demonstrate that the system can reliably identify rat head position and orientation. Evaluations using a commercial optical tracker device show that the system achieves accuracy that rivals commercial multi-camera systems. COMPARISON WITH EXISTING

methodsOur solution significantly improves upon existing monocular marker-based tracking methods, both in accuracy and in allowable range of motion.

conclusionsThe proposed system enables the study of complex behaviors by providing robust, fine-scale measurements of rodent head motions in a wide range of orientations.

Indexed as

AlgorithmsOptical DevicesAnimalsComputersMotionRatsMachine visionMotion trackingRatRodentVideo

Identifiers

PMID34968626
PMCPMC8857048
OpenAlexW4205347512

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.