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AI pipeline measures patient movement from smartphone video

By PulseAugur Editorial · [2 sources] ·

Researchers have developed Quantitative Movement Testing (QMT), a computer vision pipeline that extracts 3D kinematic biomarkers from standard smartphone videos. This method uses deep learning-based 3D pose estimation to offer a more accessible and objective alternative to costly laboratory-based motion capture systems. QMT has demonstrated high agreement with gold-standard methods and shows promise for tracking disease progression and treatment response in clinical trials, particularly for chronic pain conditions. AI

IMPACT Offers a scalable, objective biomarker for remote patient monitoring and clinical trial assessment.

RANK_REASON The cluster contains an academic paper detailing a new research methodology and its validation.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

AI pipeline measures patient movement from smartphone video

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Pranav Mahajan, Amanda Wall, Eleonora Maria Camerone, Julie Stebbins, Eoin Kelleher, Shuangyi Tong, Annina Schmid, Katja Wiech, Anushka Irani, Ben Seymour ·

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

    arXiv:2606.02301v1 Announce Type: 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 assess…

  2. arXiv cs.AI TIER_1 English(EN) · Ben Seymour ·

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

    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 res…

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