Evidence map›Paper›PMID 40732417›Full record

ArticleSensors (Basel, Switzerland)2025

A Real-Time Vision-Based Adaptive Follow Treadmill for Animal Gait Analysis.

Guanghui Li, Salif Komi, Jakob Fleng Sorensen, Rune W Berg

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Guanghui LiDepartment of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200 Copenhagen N, Denmark.ORCID 0000-0001-5917-0010
Salif KomiDepartment of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200 Copenhagen N, Denmark.
Jakob Fleng SorensenDepartment of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200 Copenhagen N, Denmark.
Rune W BergDepartment of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200 Copenhagen N, Denmark.

Funding

Chinese Scholarship Council(CSC) 202009110098Lundbeck Foundation R366-2021-233
6 · The paper itself

Abstract

Treadmills are a convenient tool to study animal gait and behavior. Traditional animal treadmill designs often entail preset speeds and therefore have reduced adaptability to animals' dynamic behavior, thus restricting the experimental scope. Fortunately, advancements in computer vision and automation allow circumvention of these limitations. Here, we introduce a series of real-time adaptive treadmill systems utilizing both marker-based visual fiducial systems (colored blocks or AprilTags) and marker-free (pre-trained models) tracking methods powered by advanced computer vision to track experimental animals. We demonstrate their real-time object recognition capabilities in specific tasks by conducting practical tests and highlight the performance of the marker-free method using an object detection machine learning algorithm (FOMO MobileNetV2 network), which shows high robustness and accuracy in detecting a moving rat compared to the marker-based method. The combination of this computer vision system together with treadmill control overcome the issues of traditional treadmills by enabling the adjustment of belt speed and direction based on animal movement.

Indexed as

Exercise TestGaitGait AnalysisAlgorithmsAnimalsMachine LearningRatsadaptive controlFOMO MobileNetV2intelligent treadmillobject trackingOpenMV4real-time computer vision

Identifiers

PMID40732417
PMCPMC12299387

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.