Bespoke Fuzzy Logic Design to Automate a Better Understanding of Running Gait Analysis

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.

Automated summary

Running gait assessment and running shoe recommendation are crucial for preventing injuries among runners with different skill levels and running styles. Traditional methods rely on trained professionals and expensive equipment, but still suffer from subjectivity and inconsistency. Therefore, there is a need for low-cost and reproducible gait assessment methods, especially in low-resource contexts and during the COVID-19 pandemic. Fuzzy logic has been proven effective and efficient in assessing and identifying gait patterns, making it ideal for embedded systems.