Determining Speed and Stride Length using an Ultrawide Bandwidth Local Positioning System

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dc.contributor.advisorStefanyshyn, Darren John
dc.contributor.authorSingh, Pratham P.
dc.contributor.committeememberBoyd, Jeffrey Edwin
dc.contributor.committeememberEdwards, William Brent
dc.contributor.committeememberFerber, Reed
dc.contributor.committeememberYanushkevich, Svetlana N.
dc.date2021-06
dc.date.accessioned2021-01-22T18:53:42Z
dc.date.available2021-01-22T18:53:42Z
dc.date.issued2021-01-13
dc.description.abstractThere are many modalities that can profile speed and stride length for runners. One such modality includes using wearable technologies. An example of a wearable technology includes a global positioning system-based wearable. However, due to its limitations, an alternative may include a local positioning system-based wearable operating in the ultrawide bandwidth. Considering that a local positioning system is not good at determining gait events such as heel and step count, applying sensor fusion with an inertial measurement unit may be beneficial. Therefore, the purpose of the dissertation was to compare speed and stride length determined from an ultrawide bandwidth local positioning system equipped with an inertial measurement unit to a criterion standard (i.e. the “gold standard”) such as video motion capture and timing gates. The data suggest that the local positioning system used in the project may not be a valid tool without further processing. Using machine learning algorithms, pertinent features from a gait cycle that can better extract speed and stride length were explored. More specifically, using a stepwise linear regression model first and then using a feedforward neural network proved to be quite successful in estimating stride length. Chapter 1 provides an introduction to the project, Chapter 2 provides a review of relevant literature, Chapter 3 provides an insight into the materials and methods used, Chapter 4 shows the results obtained from the methods described earlier, Chapter 5 is a discussion of the results obtained and Chapter 6 concludes with suggestions regarding next steps that should be taken.en_US
dc.identifier.citationSingh, P. P. (2021). Determining Speed and Stride Length using an Ultrawide Bandwidth Local Positioning System (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/38564
dc.identifier.urihttp://hdl.handle.net/1880/112991
dc.language.isoengen_US
dc.publisher.facultySchulich School of Engineeringen_US
dc.publisher.institutionUniversity of Calgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.en_US
dc.subjectLocal Positioning Systemen_US
dc.subjectMotion Captureen_US
dc.subjectMachine Learningen_US
dc.subjectGaiten_US
dc.subject.classificationEngineering--Biomedicalen_US
dc.titleDetermining Speed and Stride Length using an Ultrawide Bandwidth Local Positioning Systemen_US
dc.typedoctoral thesisen_US
thesis.degree.disciplineEngineering – Biomedicalen_US
thesis.degree.grantorUniversity of Calgaryen_US
thesis.degree.nameMaster of Science (MSc)en_US
ucalgary.item.requestcopytrueen_US
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