Quantitative Movement Testing: Measuring Patient Movements from a Single Smartphone Video
2026-06-01 • Human-Computer Interaction
Human-Computer InteractionArtificial IntelligenceComputer Vision and Pattern Recognition
AI summaryⓘ
The authors created a tool called Quantitative Movement Testing (QMT) that uses regular smartphone videos to measure how people move in 3D, which helps understand how chronic pain affects movement. They showed that QMT works almost as well as expensive, lab-only motion capture systems by testing it with healthy people and patients with fibromyalgia and sciatica. QMT reliably measured movement changes over time and could detect differences between healthy and sick people even when used at home, though home measurements were less precise. This suggests QMT could be a practical way to monitor pain-related movement issues outside the lab, but more work is needed to improve accuracy in everyday settings.
Chronic pain3D kinematicsComputer visionPose estimationMotion captureFibromyalgiaSciaticaBiomechanicsTest-retest reliabilityClinical biomarkers
Authors
Pranav Mahajan, Amanda Wall, Eleonora Maria Camerone, Julie Stebbins, Eoin Kelleher, Shuangyi Tong, Annina Schmid, Katja Wiech, Anushka Irani, Ben Seymour
Abstract
Chronic pain diminishes quality of life by decreasing functional ability, yet objectively measuring this functional impact remains challenging in real-world settings. While optical motion capture provides high precision for assessing altered movement quality, it is costly and restricted to laboratory environments. We aimed to develop and validate Quantitative Movement Testing (QMT), a computer vision pipeline extracting 3D kinematic biomarkers from standard monocular smartphone video, balancing clinical accessibility with biomechanical accuracy. We validated the QMT pipeline, utilising deep learning-based 3D pose-estimation, against gold-standard optical motion capture in healthy controls (N=13). Following leave-one-subject-out calibration to correct systematic bias, we deployed QMT in two prospective clinical cohorts to assess real-world utility: a pre- and post-intervention trial for fibromyalgia patients, and a 30-day longitudinal at-home monitoring study of chronic sciatica patients and healthy controls. In laboratory validation, QMT extracted clinical kinematic metrics with high agreement to optical motion capture, yielding strong correlations (r > 0.85) and low mean absolute errors. QMT demonstrated high test-retest reliability (r > 0.86) in fibromyalgia patients and successfully tracked day-to-day movement fluctuations in chronic sciatica. While real-world home settings introduced higher measurement variance than lab settings, QMT found group-level differences between healthy controls and sciatica patients based entirely on remote recordings. Monocular 3D pose estimation offers a scalable alternative to traditional assessments. QMT provides an objective, accessible biomarker for tracking disease progression and treatment response in clinical trials, though further research is needed to optimise reliability in home environments.