Young, Fraser, Mason, Rachel, Morris, Rosie, Stuart, Sam and Godfrey, Alan (2023) Internet-of-Things-Enabled Markerless Running Gait Assessment from a Single Smartphone Camera. Sensors, 23 (2). p. 696. ISSN 1424-3210
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Abstract
Running gait assessment is essential for the development of technical optimization strategies as well as to inform injury prevention and rehabilitation. Currently, running gait assessment relies on (i) visual assessment, exhibiting subjectivity and limited reliability, or (ii) use of instrumented approaches, which often carry high costs and can be intrusive due to the attachment of equipment to the body. Here, the use of an IoT-enabled markerless computer vision smartphone application based upon Google’s pose estimation model BlazePose was evaluated for running gait assessment for use in low-resource settings. That human pose estimation architecture was used to extract contact time, swing time, step time, knee flexion angle, and foot strike location from a large cohort of runners. The gold-standard Vicon 3D motion capture system was used as a reference. The proposed approach performs robustly, demonstrating good (ICC(2,1) > 0.75) to excellent (ICC(2,1) > 0.90) agreement in all running gait outcomes. Additionally, temporal outcomes exhibit low mean error (0.01–0.014 s) in left foot outcomes. However, there are some discrepancies in right foot outcomes, due to occlusion. This study demonstrates that the proposed low-cost and markerless system provides accurate running gait assessment outcomes. The approach may help routine running gait assessment in low-resource environments.
| Item Type: | Article |
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| Additional Information: | Funding information: This work was supported by the European Regional Development Intensive Industrial Innovation Programme (IIIP) as part of doctoral research, Grant Number: 25R17P01847. Rachel Mason is co-funded by DANU sports and the faculty of health and life sciences, Northumbria University. Dr Stuart is supported, in part, by funding from the Parkinson’s Foundation (PF-FBS-1898, PFCRA-2073). |
| Uncontrolled Keywords: | gait analysis; computer vision; deep learning; signal analysis; pose estimation; BlazePose; smartphone application |
| Subjects: | C600 Sports Science G400 Computer Science |
| Department: | Faculties > Engineering and Environment > Computer and Information Sciences Faculties > Health and Life Sciences > Sport, Exercise and Rehabilitation |
| Depositing User: | Elena Carlaw |
| Date Deposited: | 09 Jan 2023 08:39 |
| Last Modified: | 09 Jan 2023 08:45 |
| URI: | https://nrl.northumbria.ac.uk/id/eprint/51094 |
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