ArticlePloS one2023
A webcam-based machine learning approach for three-dimensional range of motion evaluation.
Article in PloS one, 2023. 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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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.
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Who cites it
8 citing papers in PubMed.
- Evaluation Protocols and Validation for Cameras in Indoor Healthcare Monitoring.Sensors (Basel, Switzerland) · 2026Article
- Markerless On-Device Detection of Compensatory Movement Patterns in Upper-Limb Rehabilitation Exercises from Monocular RGB Video: A Validation Study in Healthy Adults.Sensors (Basel, Switzerland) · 2026Article
- Validation of markerless human pose estimation methods for clinical assessment of elbow range of motion.PloS one · 2026Article
- Test-Retest Reliability and Validity of TecnoBody D-Wall to Assess the Range of Motion During Overhead Squat in Healthy Individuals.Life (Basel, Switzerland) · 2025Article
- Orthopedic surgeon level joint angle assessment with artificial intelligence based on photography: a pilot study.Biomedical engineering letters · 2025Article
- Markerless Motion Analysis Using New Digital Technology.Physical therapy research · 2025Review
- Estimation of Shoulder Joint Rotation Angle Using Tablet Device and Pose Estimation Artificial Intelligence Model.Sensors (Basel, Switzerland) · 2024Article
- Reliability and validity of computer vision-based markerless human pose estimation for measuring hip and knee range of motion.Healthcare technology lettersArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundJoint range of motion (ROM) is an important quantitative measure for physical therapy. Commonly relying on a goniometer, accurate and reliable ROM measurement requires extensive training and practice. This, in turn, imposes a significant barrier for those who have limited in-person access to healthcare.
objectiveThe current study presents and evaluates an alternative machine learning-based ROM evaluation method that could be remotely accessed via a webcam.
methodsTo evaluate its reliability, the ROM measurements for a diverse set of joints (neck, spine, and upper and lower extremities) derived using this method were compared to those obtained from a marker-based optical motion capture system.
resultsData collected from 25 healthy adults demonstrated that the webcam solution exhibited high test-retest reliability, with substantial to almost perfect intraclass correlation coefficients for most joints. Compared with the marker-based system, the webcam-based system demonstrated substantial to almost perfect inter-rater reliability for some joints, and lower inter-rater reliability for other joints (e.g., shoulder flexion and elbow flexion), which could be attributed to the reduced sensitivity to joint locations at the apex of the movement.
conclusionsThe proposed webcam-based method exhibited high test-retest and inter-rater reliability, making it a versatile alternative for existing ROM evaluation methods in clinical practice and the tele-implementation of physical therapy and rehabilitation.
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Registered trials
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