ArticleScientific reports2025
The influence of the marker set on inverse kinematics results to inform markerless motion capture annotations.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A framework for quantifying the mechanics of dexterous grasp.bioRxiv : the preprint server for biology · 2026Article
- Comprehensive human locomotion and electromyography dataset: Gait120.Scientific data · 2025Article
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3 authors.
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Abstract
Markerless motion capture has the potential to enable biomechanical analyses without specialised, high-cost equipment. However, the comparability of many markerless motion capture frameworks with the most used marker-based method is limited. One reason for this is the lack of high-quality, biomechanically-informed datasets that are needed to train markerless models. This study aimed to inform the development of such a dataset by systematically analysing the agreement between a gold-standard marker set and a reduced number of markers to solve inverse kinematics (IK). We analysed the impact of different marker positions on the IK solution using an OpenSim lower body model with real and synthetic data of running, walking and counter movement jumps. We found that one mid-segment marker in addition to two anatomical markers per segment result in the best agreement to a gold-standard marker set. The results for real and synthetic data across all movements were similar, with synthetic data showing slightly better agreement with a reduced number of markers (root mean squared error 1.55-8.27
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