AI and Computer-Vision Measurements of Running Mechanics from Smartphone Video: A Reproducibility-Centred Evaluation
DOI:
https://doi.org/10.66687/mfqw4w66Keywords:
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 difficulty is particularly visible when a system is transferred to a new setting. The objective is to test the agreement and repeatability of video-derived running metrics under conditions resembling ordinary training environments. Relevant methodological and data-quality literature informs the design of the sampling and validation strategy [3–7]. The proposed method is a repeated-measures measurement protocol comparing standardised smartphone recordings against synchronised reference markers. Comparators will include two-dimensional pose estimates, temporally smoothed estimates and manual reference measurements. 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. The reproducibility extension records dataset versions, model settings, exclusion rules and provenance before any held-out evaluation. Statistical and computational sources help define the reproducibility checks and comparison procedures [8–12]. The primary outcomes are stride frequency, stance-time estimates, joint-angle agreement and within-runner repeatability. Potential biases include camera angles, clothing occlusion, running speed, environmental lighting and participant-level validation. An independent replication exercise would repeat the main analysis from the documented artefacts and quantify disagreements. Additional research informs the prespecified stress tests and limits on interpretation [13–17]. The intended contribution is an auditable research protocol rather than an unsupported claim of effectiveness; related work frames its transferability limits [18–23].
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