AI and Computer-Vision Measurements of Running Mechanics from Smartphone Video: A Cross-Site Validation Agenda
DOI:
https://doi.org/10.66687/zbwmqx40Keywords:
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. The central research objective is 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]. To answer the question, the study proposes a repeated-measures measurement protocol comparing standardised smartphone recordings against synchronised reference markers. Enterprise-storage sources are used to specify constraints on data availability and provenance [1], with complementary infrastructure evidence defining practical recovery safeguards [2]. These citations are not treated as clinical effectiveness evidence. 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 cross-site extension holds out an entire institution or deployment location rather than relying only on random test splits. 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. Between-site heterogeneity would be examined through prespecified subgroup estimates and transparent domain-shift diagnostics. 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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