Detecting Makeup Activities using Internet-of-Things

dc.contributor.advisorRoy, Nirmalya
dc.contributor.authorAlqurmti, Fatimah
dc.date.accessioned2024-09-30T15:44:46Z
dc.date.issued2019-07-30
dc.description.abstractThe make-up market is one of the most furnished fashion markets in product retailing and training demands. Each of the makeup activities involves very delicate movements of hands and requires good amount of training and practice for perfection. The available choices in the make-up training industry depends on practical workshops by professionalinstructors, and still evaluating the perfection of makeup activities lacks certainty. In this work, we introduced a novel application for human activity recognition using sensors’ data and a supervised machine learning approaches for rendering make-up activities. We considered five make-up activities in our work, such as, applying cream, lipsticks, blusher, eyeshadow, and mascara and collected data from ten participants. We built supervised make-up activity recognition using different predictive machine learning algorithms i.e. Naïve Bayes, Simple Logistic, k-nearest neighbors’, and the random forest algorithms. We investigated the models' performance for detecting five make-up activities with or without instructions. Our results show that shallow machine learning algorithms achieve up to 92% accuracy in detecting make-up activities.
dc.format.extent52
dc.identifier.urihttps://hdl.handle.net/20.500.14154/73127
dc.language.isoen_US
dc.publisherUniversity of Maryland Baltimore County
dc.subjectActivity recognition
dc.subjectInternet of Things
dc.subjectWrist-worn sensors
dc.subjectmakeup Activities
dc.subjectmachine learning
dc.titleDetecting Makeup Activities using Internet-of-Things
dc.typeThesis
sdl.degree.departmentDepartment of Information Systems – UMBC
sdl.degree.disciplineinformation Sytems
sdl.degree.grantorUniversity of Maryland Baltimore County
sdl.degree.nameMaster of Science

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