AI and Computer-Vision Measurements of Running Mechanics from Smartphone Video
DOI:
https://doi.org/10.66687/7ytayj39Keywords:
Artificial Intelligence, Machine Learning, Medical Informatics, Decision Support Systems, Clinical, Reproducibility of ResultsAbstract
Smartphone pose-estimation tools offer accessible measurements of running movement but may be sensitive to camera position, speed and illumination. This gap has consequences for interpreting performance outside carefully curated evaluation settings. The research question is whether it is possible to test the agreement and repeatability of video-derived running metrics under conditions resembling ordinary training environments. Previous work provides context for sampling, input validation and study boundaries [3–7]. The planned analysis uses a repeated-measures measurement protocol comparing standardised smartphone recordings against synchronised reference markers. The technical pipeline must remain auditable: prior work on storage operations provides system-level context [1], while a second source identifies infrastructure conditions relevant to data continuity [2], rather than patient-level outcomes. Under matched evaluation conditions, the protocol compares two-dimensional pose estimates, temporally smoothed estimates and manual reference measurements. Methodological sources are used to specify transparent comparison and reporting practices [8–12]. The design prioritises precise operational definitions and a straightforward baseline before introducing more complex alternatives. Performance will be judged using stride frequency, stance-time estimates, joint-angle agreement and within-runner repeatability. Interpretation will account for camera angles, clothing occlusion, running speed, environmental lighting and participant-level validation. Primary analyses would be reported by the main deployment or participant subgroup so that averaged results do not conceal systematic failures. The supplementary technical literature is used to identify plausible failure mechanisms [13–17]. No study result is assumed in advance; the analysis is designed to show both when the method helps and when it fails, with reporting context from the remaining sources [18–23].
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