ArticleAnnals of biomedical engineering2020
Quantifying Soft Tissue Artefacts and Imaging Variability in Motion Capture of the Fingers.
Article in Annals of biomedical engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed, 19 citations in OpenAlex.
- Biomechanical Characterization of Metacarpal Fixation: Internal Load Determination and Evaluation of a Novel Adhesive Osteosynthesis.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2025Article
- "How would you handle this?" The impact of embedding early patient and public involvement in a biomechanical computational engineering doctoral research project.Research involvement and engagement · 2025Article
- Thumb motion is not the same as first carpometacarpal joint motion.Journal of biomechanics · 2025Article
- Sensor-assessed grasping time as a biomarker of functional impairment in rheumatoid arthritis.Scientific reports · 2025Article
- An instrumented approach to quantify wrist and finger flexor spasticity: A study protocol.PloS one · 2025Article
- OpenHands: An Open-Source Statistical Shape Model of the Finger Bones.Annals of biomedical engineering · 2024Article
- Lumbar segment-dependent soft tissue artifacts of skin markers duringFrontiers in bioengineering and biotechnology · 2022Article
- Optical motion capture accuracy is task-dependent in assessing wrist motion.Journal of biomechanics · 2021Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
This study assessed the accuracy of marker-based kinematic analysis of the fingers, considering soft tissue artefacts (STA) and marker imaging uncertainty. We collected CT images of the hand from healthy volunteers with fingers in full extension, mid- and full-flexion, including motion capture markers. Bones and markers were segmented and meshed. The bone meshes for each volunteer's scans were aligned using the proximal phalanx to study the proximal interphalangeal joint (PIP), and using the middle phalanx to study the distal interphalangeal joint (DIP). The angle changes between positions were extracted. The HAWK protocol was used to calculate PIP and DIP joint flexion angles in each position based on the marker centroids. Finally the marker locations were 'corrected' relative to the underlying bones, and the flexion angles recalculated. Static and dynamic marker imaging uncertainty was evaluated using a wand. A strong positive correlation was observed between marker- and CT-based joint angle changes with 0.980 and 0.892 regression slopes for PIP and DIP, respectively, and Root Mean Squared Errors below 4°. Notably for the PIP joint, correlation was worsened by STA correction. The 95% imaging uncertainty interval was < ± 1° for joints, and < ± 0.25 mm for segment lengths. In summary, the HAWK marker set's accuracy was characterised for finger joint flexion angle changes in a small group of healthy individuals and static poses, and was found to benefit from skin movements during flexion.
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Registered trials
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