ArticleScientific data2026
Motion capture dataset of 137 post-operative total hip replacement patients performing activities of daily living.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Motion capture dataset of 137 post-operative total hip replacement patients performing activities of daily living.Scientific data · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Three-dimensional motion capture is a powerful tool in clinical and engineering applications, but it can be time consuming, expensive, and difficult to access patient populations. Here, we present an open-access dataset comprising 3D motion capture and ground reaction force data from 137 post-operative total hip replacement patients performing eight activities of daily living (ADLs): normal walking, fast walking, stair ascent, stair descent, sit-to-stand, stand-to-sit, lunge, and squat. Data were collected using a 10-camera Vicon system and two AMTI force plates, and are provided in C3D and txt file format, compatible with inverse dynamic analysis platforms (e.g Visual 3D, MOKKA) and musculoskeletal modelling platforms (e.g. OpenSim, AnyBody). Each file includes synchronised marker trajectories, analog force data, and participant metadata (e.g. age, sex, BMI, operated limb, time since surgery). This dataset offers a unique opportunity to study functional biomechanics across a range of ADLs, enabling applications in musculoskeletal modelling, rehabilitation assessment, and the development of machine learning algorithms.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.