Running Pattern Recognition in a Comfortable and an Uncomfortable Shoe Condition
Date
2018-12-07
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Abstract
The diverse development of running shoes has mostly been driven by three functional factors: reducing injury risk, increasing performance, and increasing perceived comfort. The few studies that focused on comfort provide contradicting results. Comfort is a subjective impression, different for every individual, and is speculated to be one of the most important features of a running shoe, however, it is not well understood or quantifiable. As a result, comfort and its relationship to running biomechanics has not been established. The purpose of this study was to (a) distinguish movement patterns while running in a comfortable shoe condition from movement patterns while running in an uncomfortable shoe condition, (b) determine if these patterns are localized to a specific body segment, and (c) determine which classification tool (linear or spherical) yields the most conclusive results. The movement patterns while running in two different shoes were compared and classified using a support vector machine and spherical classification. The classifications were performed using accelerations and angular velocities from all five sensor locations as well as using subsets of the data. The highest classification (61.88%) was found using spherical classification and a subset of the data. Both classification tools resulted in low success rates. The running kinematics in this study were unaffected by a change in comfort. Keywords: running, pattern recognition, comfort, shoes, inertial measurement units
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Keywords
running, pattern recognition, comfort, shoes, inertial measurement units
Citation
Manz, S. (2018). Running Pattern Recognition in a Comfortable and an Uncomfortable Shoe Condition (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/34924