AI and Computer-Vision Measurements of Running Mechanics from Smartphone Video: A Governance-Focused Research Design
DOI:
https://doi.org/10.66687/0qfk8334Keywords:
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 governance component assigns a decision owner and defines when automated suggestions require human sign-off. 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. The analysis would compare technical performance with traceability, access controls and practical accountability. 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].
References
[1] Mohammed Nazir. (2023). Performance-Telemetry-Based Anomaly Detection for Early Failure Identification in Enterprise Storage Systems. Journal of Multidisciplinary Research and Integrated Sciences, 3(1), 59–70. https://jmris.com/index.php/jmris/article/view/2023V1P6
[2] Mohammed Nazir. (2021). Automating Enterprise Storage Provisioning with PowerShell and Python: Effects on Deployment Time and Configuration Errors. Journal of Multidisciplinary Research and Integrated Sciences, 1(1), 43–55. https://jmris.com/index.php/jmris/article/view/2021P1V6
[3] Azam, M. A., Ansari, I., Haque, G. M. M., & Jahid, A. (2026). Leveraging Health Information Systems and Predictive Analytics to Improve Patient Outcomes: A Data-Driven Approach. The American Journal of Medical Sciences and Pharmaceutical Research, 8(3), 45–70. https://theamericanjournals.com/index.php/tajmspr/article/view/7512
[4] 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
[5] Johnson, A., Pollard, T., & Mark, R. (2016). MIMIC-III Clinical Database, version 1.4. PhysioNet. https://physionet.org/content/mimiciii/1.4/
[6] Nahid, M. A. U., Azam, M. A., Ratul, A. S., et al. (2026). Machine Learning Meets XAI: Grad-CAM Visualization for Enhanced Lung and Colon Cancer Detection. Proceedings of the IEEE 2nd International Conference on Quantum Photonics, Artificial Intelligence & Networking. https://ieeexplore.ieee.org/document/11546191/
[7] 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
[8] 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
[9] 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
[10] 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
[11] 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
[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] 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
[14] Khan, M. F. I., Begum, M. H., Rahman, M. A., Limon, G. Q., Azam, M. A., & Masum, A. K. M. (2025). A comprehensive review of advances in transformer, GAN, and attention mechanisms: Their role in multimodal learning and applications across NLP. International Journal of Science and Research Archive, 15(1), 454–459. https://ijsra.net/content/comprehensive-review-advances-transformer-gan-and-attention-mechanisms-their-role
[15] Kaur, G., Azam, M. A., & Roy, K. K. (2026). Data Mesh and Data Fabric in Large Enterprises: Transforming Scalable Data Governance and Analytics. The American Journal of Engineering and Technology, 8(9), 58–85. https://usajournal.net/index.php/tujtajet/article/view/1879
[16] Haque, G. M. M., Ansari, I., Bhujel, K., Jahid, A., & Azam, M. A. (2026). Digital Transformation Strategies and IT Governance: Aligning Business Value with Technology Investments. The American Journal of Management and Economics Innovations, 8(3), 24–48. https://theamericanjournals.com/index.php/tajmei/article/view/7580
[17] Jiao, Z. et al. (2024). The Design and Implementation of a Capacity-Variant Storage System. USENIX FAST '24. https://www.usenix.org/conference/fast24/presentation/jiao
[18] 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
[19] Dai, Y. et al. (2024). Symbiosis: The Art of Application and Kernel Cache Cooperation. USENIX FAST '24. https://www.usenix.org/conference/fast24/presentation/dai
[20] Wong, D.L.-K. et al. (2024). Baleen: ML Admission & Prefetching for Flash Caches. USENIX FAST '24. https://www.usenix.org/conference/fast24/presentation/wong
[21] Zhou, W. et al. (2025). 3L-Cache: Low Overhead and Precise Learning-based Eviction Policy for Caches. USENIX FAST '25. https://www.usenix.org/conference/fast25/presentation/zhou-wenbin
[22] Li, T. et al. (2024). MinFlow: High-performance and Cost-efficient Data Passing for I/O-intensive Stateful Serverless Analytics. USENIX FAST '24. https://www.usenix.org/conference/fast24/presentation/li
[23] Jiao, B., Goel, A. & Wang, A.-I.A. (2025). Silhouette: Leveraging Consistency Mechanisms to Detect Bugs in Persistent Memory-Based File Systems. USENIX FAST '25. https://www.usenix.org/conference/fast25/presentation/jiao
Published
Issue
Section
License
Copyright (c) 2026 Amy Green
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright in their published work. Unless an individual Version of Record identifies different terms, JMRIS articles are distributed under the Creative Commons Attribution 4.0 International License (CC BY 4.0): https://creativecommons.org/licenses/by/4.0/. This permits sharing and adaptation, including commercial use, with appropriate attribution, a link to the license, and indication of changes. Third-party material may be subject to separate terms. Existing articles retain the license originally granted to their Version of Record; a policy update does not retrospectively replace that license.
