AI and Computer-Vision Measurements of Running Mechanics from Smartphone Video: An Audit-Ready Methods Framework
DOI:
https://doi.org/10.66687/qnj8nw53Keywords:
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 audit-focused extension preserves processing logs, model decisions and exception handling for independent review. 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. Uncertain outcomes would be adjudicated with a predefined rubric, rather than silently recoded as successes. 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].
References
[1] Mohammed Nazir. (2022). Automated SnapMirror Migration from On-Premises Storage to Cloud Volumes ONTAP: A Replication Performance Study. Journal of Multidisciplinary Research and Integrated Sciences, 2(1), 61–72. https://jmris.com/index.php/jmris/article/view/2022V1P6
[2] Mohammed Nazir. (2022). SVM-Based Disaster Recovery for Hybrid Enterprise Storage: Effects on Recovery Time and Service Availability. Journal of Computing, Intelligence and Information Sciences, 3(1). https://jciis.com/index.php/jciis/article/view/2022-01-05
[3] Esteva, A., Kuprel, B., Novoa, R. A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542, 115–118. https://www.nature.com/articles/nature21056
[4] Johnson, A., Pollard, T., & Mark, R. (2016). MIMIC-III Clinical Database, version 1.4. PhysioNet. https://physionet.org/content/mimiciii/1.4/
[5] Singhal, K., Azizi, S., Tu, T., et al. (2023). Large language models encode clinical knowledge. Nature, 620, 172–180. https://www.nature.com/articles/s41586-023-06291-2
[6] McKinney, S. M., Sieniek, M., Godbole, V., et al. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577, 89–94. https://www.nature.com/articles/s41586-019-1799-6
[7] Begum, M. H., Talukder, S. I., Ahmed, M. J., Khan, M. F. I., & Azam, M. A. (2025). XAI-Driven Multimodal Deep Learning for Early Sepsis Prediction in ICU. Journal of Science Technology and Social Transformation, 1(1), 30–41. https://jstst.info/j/article/view/25
[8] Jumper, J., Evans, R., Pritzel, A., et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596, 583–589. https://www.nature.com/articles/s41586-021-03819-2
[9] Liu, X., Faes, L., Kale, A. U., et al. (2020). Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nature Medicine, 26, 1364–1374. https://www.nature.com/articles/s41591-020-1034-x
[10] Lundberg, S. M., & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems, 30. https://arxiv.org/abs/1705.07874
[11] Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, 30. https://arxiv.org/abs/1706.03762
[12] Guo, C. et al. (2017). On Calibration of Modern Neural Networks. Proceedings of Machine Learning Research, 70, 1321–1330. https://proceedings.mlr.press/v70/guo17a.html
[13] Shen, J. et al. (2023). FUSEE: A Fully Memory-Disaggregated Key-Value Store. USENIX FAST '23. https://www.usenix.org/conference/fast23/presentation/shen
[14] Pang, G. et al. (2020). Deep Learning for Anomaly Detection: A Review. arXiv:2007.02500. https://arxiv.org/abs/2007.02500
[15] Azam, M. A., Hossain, I., & Islam, M. (2024). Advancing Explainable Artificial Intelligence and Predictive Business Analytics to Strengthen Digital Transformation, Operational Resilience, and Productivity among U.S. Small and Medium-Sized Enterprises. European Journal of Management, Economics and Business, 1(1), 70–94. https://ejmeb.com/index.php/journal/article/view/323
[16] Cao, Z., Kuenning, G. & Zadok, E. (2020). Carver: Finding Important Parameters for Storage System Tuning. USENIX FAST '20. https://www.usenix.org/conference/fast20/presentation/cao-zhen
[17] Mahdisoltani, F., Stefanovici, I. & Schroeder, B. (2017). Proactive Error Prediction to Improve Storage System Reliability. USENIX ATC '17. https://www.usenix.org/conference/atc17/technical-sessions/presentation/mahdisoltani
[18] Cidon, A. et al. (2013). Copysets: Reducing the Frequency of Data Loss in Cloud Storage. USENIX ATC '13. https://www.usenix.org/conference/atc13/technical-sessions/presentation/cidon
[19] Nachman, A., Yadgar, G. & Sheinvald, S. (2020). GoSeed: Generating an Optimal Seeding Plan for Deduplicated Storage. USENIX FAST '20. https://www.usenix.org/conference/fast20/presentation/nachman
[20] Levi, A. et al. (2024). Physical vs. Logical Indexing with IDEA: Inverted Deduplication-Aware Index. USENIX FAST '24. https://www.usenix.org/conference/fast24/presentation/levi
[21] Alagappan, R. et al. (2019). Protocol-Aware Recovery for Consensus-Based Storage. USENIX ATC '19. https://www.usenix.org/conference/atc19/presentation/alagappan
[22] Patel, T. et al. (2020). Uncovering Access, Reuse, and Sharing Characteristics of I/O-Intensive Files on Large-Scale Production HPC Systems. USENIX FAST '20. https://www.usenix.org/conference/fast20/presentation/patel-hpc-systems
[23] Yang, Z. et al. (2023). λ-IO: A Unified IO Stack for Computational Storage. USENIX FAST '23. https://www.usenix.org/conference/fast23/presentation/yang-zhe
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