Just Find It: The Mymo Approach to Recommend Running Shoes

Fraser Young, Graham Coulby, Ian Watson, Craig Downs, Samuel Stuart, Alan Godfrey

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
139 Downloads (Pure)

Abstract

Wearing inappropriate running shoes may lead to unnecessary injury through continued strain upon the lower extremities; potentially damaging a runner's performance. Many technologies have been developed for accurate shoe recommendation, which centre on running gait analysis. However, these often require supervised use in the laboratory/shop or exhibit too high a cost for personal use. This work addresses the need for a deployable, inexpensive product with the ability to accurately assess running shoe-type recommendation. This was achieved through quantitative analysis of the running gait from 203 individuals through use of a tri-axial accelerometer and tri-axial gyroscope-based wearable (Mymo). In combination with a custom neural network to provide the shoe-type classifications running within the cloud, we experience an accuracy of 94.6% in classifying the correct type of shoe across unseen test data.

Original languageEnglish
Article number9115589
Pages (from-to)109791-109800
Number of pages10
JournalIEEE Access
Volume8
Early online date12 Jun 2020
DOIs
Publication statusPublished - 24 Jun 2020

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