There 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.