Bespoke fuzzy logic design to automate a better understanding of running gait analysis

Young, Fraser, Stuart, Sam, McNicol, Robert, Morris, Rosie, Downs, Craig, Coleman, Martin and Godfrey, Alan (2022) Bespoke fuzzy logic design to automate a better understanding of running gait analysis. IEEE Journal of Biomedical and Health Informatics. ISSN 2168-2194 (In Press)

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Official URL: https://doi.org/10.1109/JBHI.2022.3189594

Abstract

Running gait assessment and running shoe recommendation is important for the injury prevention of runners who exhibit different skill-levels and running styles. Traditionally, running gait assessment for shoe recommendation relies upon a combination of trained professionals (e.g., sports-therapists, physiotherapists) and complex equipment such as motion or pressure sensors, often incurring a high-cost to the consumer. Despite this, assessments are still prone to subjectivity, and may differ between assessors. Alternatively, methods to provide low-cost, reproduceable gait assessment has become a necessity, especially within a habitual (low-resource) context, with many traditional methods generally unavailable due to the need of professional assistance and more recently the COVID-19 pandemic. Fuzzy logic has shown to be an effective tool in the assessment and identification of gait by providing the potential for a high-accuracy methodology, while retaining a low computational cost; ideal for applications within embedded systems. Here, we present a novel shoe recommendation fuzzy inference system from the classification of two key running gait parameters, foot strike and pronation from a single foot mounted internet of thing (IoT) enabled wearable inertial measurement unit. The fuzzy approach provides excellent (ICC > 0.9) accuracy, while significantly increasing the resolution of the gait assessment technique, providing a more detailed running gait analysis.

Item Type: Article
Additional Information: Funding information: Work was supported by Northumbria University and European Regional Development Fund Intensive Industrial Innovation Programme. Sponsoring small to medium enterprise for this programme was Mymo Group Ltd. and was delivered through Northumbria University (grant number: 25R17P01847).
Uncontrolled Keywords: Fuzzy logic, embedded systems, gait assessment, wearable, IMU, running, sports therapy
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: John Coen
Date Deposited: 06 Jul 2022 09:14
Last Modified: 26 Jul 2022 07:30
URI: http://nrl.northumbria.ac.uk/id/eprint/49486

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