Markerless 3D kinematics and force estimation in cheetahs.

Zico da Silva, Stacey Shield, Penny E Hudson, Alan M Wilson, Fred Nicolls, Amir Patel

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Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Zico da SilvaDepartment of Electrical Engineering, University of Cape Town, Cape Town, 7700, South Africa. zicods7@gmail.com.

Stacey ShieldDepartment of Electrical Engineering, University of Cape Town, Cape Town, 7700, South Africa.

Penny E HudsonInstitute of Sport Nursing and Allied Health, University of Chichester, Chichester, PO19 6PE, UK.

Alan M WilsonStructure and Motion Laboratory, The Royal Veterinary College, London, NW1 0TU, UK.

Fred NicollsDepartment of Electrical Engineering, University of Cape Town, Cape Town, 7700, South Africa.

Amir PatelDepartment of Electrical Engineering, University of Cape Town, Cape Town, 7700, South Africa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The complex dynamics of animal manoeuvrability in the wild is extremely challenging to study. The cheetah (Acinonyx jubatus) is a perfect example: despite great interest in its unmatched speed and manoeuvrability, obtaining complete whole-body motion data from these animals remains an unsolved problem. This is especially difficult in wild cheetahs, where it is essential that the methods used are remote and do not constrain the animal's motion. In this work, we use data obtained from cheetahs in the wild to present a trajectory optimisation approach for estimating the 3D kinematics and joint torques of subjects remotely. We call this approach kinetic full trajectory estimation (K-FTE). We validate the method on a dataset comprising synchronised video and force plate data. We are able to reconstruct the 3D kinematics with an average reprojection error of 17.69 pixels (62.94% PCK using the nose-to-eye(s) length segment as a threshold), while the estimates produce an average root-mean-square error of 171.3N (

Indexed as

AcinonyxAnimalsBiomechanical PhenomenaImaging, Three-DimensionalLocomotionTorqueVideo RecordingInverse dynamicsPose estimationTrajectory optimisation

Identifiers

PMID38720014

DOI10.1038/s41598-024-60731-1

PMCPMC11584894

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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.