Can footwear satisfaction be predicted from mechanical properties?

Salzano, Matthew; Weir, Gillian; Thompson, Jessica; Trudeau, Matthieu B.; Ertel, Christopher; Dear, Kayley; Willwacher, Steffen; Hamill, Joseph

doi:10.1080/19424280.2022.2077843

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2023

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“…Foot scanning technologies, for instance, used sensors and cameras to capture a 3D image of the foot, providing a more accurate and comprehensive measurement. Moreover, with the integration of big data analytics, it became possible to aggregate measurements from a vast array of individuals, leading to the creation of more standardized and personalized size charts [4].…”

Section: Literature Reviewmentioning

confidence: 99%

Revolutionizing Footwear Recommendations: A Data-Driven Approach Harnessing Advanced Machine Learning Techniques

Zhuang

2023

TCSISR

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The footwear landscape is evolving. Individuals seek a personalized shoe and insole fit for enhanced comfort and health. Historically, footwear sizes were measured manually. This traditional method faced challenges in scalability and precision. The study leveraged big data and machine learning to refine shoe size recommendations. Data was sourced from online platforms, foot scanning devices, and user feedback. Rigorous preprocessing ensured the data's consistency and normalization. Multiple machine learning models were evaluated, with the Random Forest algorithm emerging as the most effective. The findings highlighted an improvement in recommendation accuracy.The research indicates that the integration of technology and data holds the potential to transform the footwear industry, prioritizing comfort and health.

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Section: Literature Reviewmentioning

confidence: 99%