Parametric and non-parametric classification methods with application to accelerometer data

Cardinal Scholar

Show simple item record

dc.contributor.advisor Begum, Munni, 1970-
dc.contributor.author Hossain, Shafayet Shariar
dc.date.accessioned 2020-08-05T19:25:34Z
dc.date.available 2020-08-05T19:25:34Z
dc.date.issued 2020-05-02
dc.identifier.uri http://cardinalscholar.bsu.edu/handle/123456789/202160
dc.description Access to thesis is permanently restricted to Ball State community only en_US
dc.description.abstract Physical Activity has been associated with fatal diseases such as cancer and heart dis- ease. Therefore, prediction of physical activity intensity levels is of paramount impor- tance. This study utilizes raw accelerometer data of 28 healthy individuals of ages 18-79 years to predict physical activity intensity levels. Parametric and non-parametric ap- proaches were used to develop predictive models from the accelerometer data. As a parametric approach the proportional odds cumulative logit model was considered both in the frequentist and Bayesian paradigm. Tree-based methods: bagging, random forest, boosting and Generalized Unbiased Interaction Detection and Estimation (GUIDE) were considered as non-parametric methods. All the models were built from four accelerom- eter placements, i.e., left wrist, right wrist, right hip and right ankle. In terms of the placement, right ankle has signi cantly highest prediction accuracy and right wrist has the lowest prediction accuracy. The parametric and non-parametric models have mixed performance with no particular method outperforming the others. However, considering overall prediction accuracy, the proportional odds cumulative logit model has the highest prediction accuracy. ii en_US
dc.description.sponsorship Department of Mathematical Sciences
dc.subject.lcsh Parameter estimation.
dc.subject.lcsh Accelerometers.
dc.subject.lcsh Exercise -- Measurement -- Mathematical models.
dc.title Parametric and non-parametric classification methods with application to accelerometer data en_US
dc.description.degree Thesis (M.S.) en_US


Files in this item

This item appears in the following Collection(s)

  • Master's Theses [5454]
    Master's theses submitted to the Graduate School by Ball State University master's degree candidates in partial fulfillment of degree requirements.

Show simple item record

Search Cardinal Scholar


Browse

My Account