Quantitative Movement Testing: Measuring Chronic Pain Patient Movements from a Single Smartphone Video
arXiv:2606.02301v2 Announce Type: replace-cross 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
The proliferation of high-quality smartphone cameras and advances in computer vision algorithms are enabling new applications in health monitoring outside of traditional clinical settings.
This development allows for objective, accessible, and frequent monitoring of chronic pain and functional ability, moving diagnostics and progress tracking closer to the patient's daily life.
The ability to perform quantitative movement analysis using common consumer devices significantly lowers the barrier to entry for biomechanical assessment, making it more scalable and less reliant on specialized labs.
- · Chronic pain patients
- · Telemedicine platforms
- · Smartphone manufacturers
- · Computer vision developers
- · Traditional motion capture labs (for routine monitoring)
- · Subjective pain assessment methods
Remote, objective monitoring of physical function becomes more widespread for chronic conditions.
Data collected from QMT could inform personalized treatment plans and enable proactive interventions.
This technology could reduce healthcare costs associated with clinic visits and improve patient outcomes through continuous feedback.
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Read at arXiv cs.AI