Title: Quantitative Movement Testing: Measuring Patient Movements from a Single Smartphone Video

Abstract:

Chronic pain significantly impairs quality of life by reducing functional capacity, yet objectively quantifying this functional decline in everyday environments remains a persistent challenge. Although optical motion capture systems offer high-precision assessment of altered movement patterns, their high cost and confinement to laboratory settings limit their practical utility. To address this, we developed and validated Quantitative Movement Testing (QMT), a computer vision framework designed to extract 3D kinematic biomarkers from standard monocular smartphone footage, thereby bridging the gap between clinical accessibility and biomechanical precision.

The QMT pipeline, which relies on deep learning-driven 3D pose estimation, was validated against gold-standard optical motion capture using a cohort of healthy controls (N=13). After implementing leave-one-subject-out calibration to mitigate systematic bias, we evaluated the real-world applicability of QMT through two prospective clinical studies: a pre- and post-intervention trial involving patients with fibromyalgia, and a 30-day longitudinal at-home monitoring study comparing chronic sciatica patients with healthy controls.

Laboratory validation results indicated that QMT-derived clinical kinematic metrics showed strong concordance with optical motion capture data, characterized by high correlation coefficients (r > 0.85) and low mean absolute errors. In clinical applications, QMT exhibited robust test-retest reliability (r > 0.86) among fibromyalgia patients and effectively monitored daily movement variations in those with chronic sciatica. Although measurements taken in home environments displayed greater variance than those obtained in controlled lab settings, QMT successfully identified group-level distinctions between healthy controls and sciatica patients based solely on remote video data. These findings suggest that monocular 3D pose estimation serves as a scalable substitute for conventional assessment methods. QMT offers an objective, widely accessible tool for monitoring disease progression and treatment efficacy in clinical trials, although additional research is required to enhance its reliability in domestic settings.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC