Research Data Leeds Repository

Citation

Lunn, David, de Pieri, Enrico, Chapman, Graham, Lund, Morten E., Redmond, Anthony and Ferguson, Stephen J. (2025) Motion capture dataset of 137 post-operative total hip replacement patients performing activities of daily living. University of Leeds. [Dataset] https://doi.org/10.5518/1701

Dataset description

This dataset comprises comprehensive motion capture and ground reaction force data from 137 post-operative total hip replacement (THR) patients. Data were collected between 2013 and 2015 using a 10-camera Vicon system and dual force plates during a range of activities of daily living (ADLs), including level walking, stair negotiation, sit-to-stand, lunge, and squat tasks. Marker trajectories and ground reaction forces were processed using standard biomechanical techniques and segmented into key movement events. The dataset has previously been used to identify biomechanical differences in THR patients by age, gender, BMI, and function, and to explore joint contact forces and their impact on bone loading. It is now released for wider use in musculoskeletal modelling, with potential application in AI and machine learning model development. This study was supported by the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement no. GA-310477 LifeLongJoints. A small subset of this dataset (comprising a limited number of participants and trials) was previously made available under DOI 10.5518/319 as part of our previous publication. The current dataset represents the complete release, encompassing all participants and all recorded activities of daily living collected under the same methodological framework. The overlap is therefore limited to a few shared example files used for demonstration in the earlier publication.

Additional information:	This dataset is associated with a paper undergoing peer review and may change. Any updates to the data will be noted here.
Divisions:	Faculty of Medicine and Health > School of Medicine
Related resources:	Location Type https://doi.org/10.5518/319 Dataset
License:	Creative Commons Attribution 4.0 International (CC BY 4.0)
Date deposited:	07 Nov 2025 15:32
URI:	https://archive.researchdata.leeds.ac.uk/id/eprint/1488
Additional details Additional details Creators or authors: Creators ORCID Other ID Email Primary affiliation Primary affiliation ID Primary affiliation ID type Secondary affiliation Secondary affiliation ID Secondary affiliation ID type Lunn, David https://orcid.org/0000-0002-3158-0496 University of Leeds https://ror.org/024mrxd33 ror de Pieri, Enrico https://orcid.org/0000-0003-1077-6294 ETH Zurich https://ror.org/05a28rw58 ror Chapman, Graham https://orcid.org/0000-0003-3983-6641 University of Leeds https://ror.org/024mrxd33 ror Lund, Morten E. https://orcid.org/0000-0001-9920-4051 AnyBody Technology (Denmark) https://ror.org/01rkdw075 ror Redmond, Anthony https://orcid.org/0000-0002-8709-9992 University of Leeds https://ror.org/024mrxd33 ror Ferguson, Stephen J. https://orcid.org/0000-0003-0650-5889 ETH Zurich https://ror.org/05a28rw58 ror Type of data: Dataset Research funders: European Union Grant numbers: GA-310477 Publication date: 2025 Resource language: English Metadata language: English Research project title: LifeLongJoints Publisher: University of Leeds Contact email: david.e.lunn@leedsbeckett.ac.uk Last Modified: 07 Nov 2025 17:27

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