ArticleScientific data2022
Functional movement screen dataset collected with two Azure Kinect depth sensors.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Comparison of hip and knee range of motion measurements using Azure Kinect and a digital goniometer in elite female weightlifters.Scientific reports · 2026Article
- Injury risk assessment using the functional movement screen in college physical education majors: a prospective cohort study.Frontiers in rehabilitation sciences · 2026Article
- Objective classification of deep squat and hurdle step scores in functional movement screen: a 3D motion capture and machine learning approach.Frontiers in bioengineering and biotechnology · 2026Article
- Association between functional movement quality, core stability, and athletic performance in male university basketball players.Frontiers in sports and active living · 2026Article
- Comparative analysis of Microsoft Kinect Azure and manual measurement methods in the sit and reach test among elite female weightlifters.Scientific reports · 2025Article
- Analysis of Kinect-Based Human Motion Capture Accuracy Using Skeletal Cosine Similarity Metrics.Sensors (Basel, Switzerland) · 2025Article
- LLM-FMS: A fine-grained dataset for functional movement screen action quality assessment.PloS one · 2025Article
- Markerless vision-based functional movement screening movements evaluation with deep neural networks.iScience · 2024Article
- Pilot Study: The Relationship between Foot Posture and Movement Quality in Non-Professional Male Football Players.Life (Basel, Switzerland) · 2023Article
- Three-Dimensional Motion Capture Data of a Movement Screen from 183 Athletes.Scientific data · 2023Article
- Functional movement screen dataset collected with two Azure Kinect depth sensors.Scientific data · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
This paper presents a dataset for vision-based autonomous Functional Movement Screen (FMS) collected from 45 human subjects of different ages (18-59 years old) executing the following movements: deep squat, hurdle step, in-line lunge, shoulder mobility, active straight raise, trunk stability push-up and rotary stability. Specifically, shoulder mobility was performed only once by different subjects, while the other movements were repeated for three episodes each. Each episode was saved as one record and was annotated from 0 to 3 by three FMS experts. The main strength of our database is twofold. One is the multimodal data provided, including color images, depth images, quaternions, 3D human skeleton joints and 2D pixel trajectories of 32 joints. The other is the multiview data collected from the two synchronized Azure Kinect sensors in front of and on the side of the subjects. Finally, our dataset contains a total of 1812 recordings, with 3624 episodes. The size of the dataset is 190 GB. This dataset provides the opportunity for automatic action quality evaluation of FMS.
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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.