Background: Generalized joint hypermobility (GJH) is often challenging to assess, but its presence could suggest a syndromic diagnosis of Ehlers-Danlos Syndromes (EDS).
Objective: An automated and objective method for estimating joint hypermobility with Beighton score using short video clips is proposed.
Method: A total of 225 adults (91.8% female, median age 32.0, range 18-64) referred to a specialized EDS clinic were recruited for this study. A video-based method relying on pose-estimation libraries was developed to predict per-joint hypermobility of both elbows, knees, fifth fingers, thumbs, and spine; as well as the overall Beighton score. The system was developed on the first 100 individuals (training set), and validated on the remaining 125 individuals (test set).
Results: The system screened out 31.9% of the training set and 32.0% of the test set as not having GJH, while recalling 89.1% and 91.9% of the true positives on the train and test set, respectively. The consistency of the system between the training and test sets suggests that it generalizes well to unseen individuals. The system was tuned to be with a focus on sensitivity to avoid screening out individuals with GJH. As such, the specificity of the system is 52.1% on the training set and 42.4% on the test set.
Conclusion: The proposed system can objectively screen individuals for possible GJH and also screen out those without GJH during the referral process, reducing the burden on specialized EDS clinics while providing early diagnostic triage. Future research will focus on deploying the tool as a mobile application.
Keywords: Ehlers-Danlos syndromes; Generalized joint hypermobility; Human pose-estimation; Range of motion estimation; Video-based goniometry.
© 2026. The Author(s).