Title:
Analyzing and Learning Movement Through Human-Computer Co-Creative Improvisation and Data Visualization

dc.contributor.advisor Magerko, Brian
dc.contributor.author Liu, Lucas
dc.contributor.committeeMember Riedl, Mark
dc.contributor.department Computer Science
dc.date.accessioned 2022-02-11T15:15:29Z
dc.date.available 2022-02-11T15:15:29Z
dc.date.created 2020-12
dc.date.issued 2020-12
dc.date.submitted December 2020
dc.date.updated 2022-02-11T15:15:29Z
dc.description.abstract Recent years have seen an incredible rise in the availability of household motion and video capture technologies, ranging from the humble webcam to the relatively sophisticated Kinect sensor. Naturally, this precipitated a rise in both the quantity and quality of motion capture data available on the internet. The wealth of data on the internet has caused a new interest in the field of motion data classification, the specific task of having a model classify and sort different clips of human motion. However, there is comparatively little work in the field of motion data clustering, which is an unsupervised field that may be more useful in the future as it allows for agents to recognize “categories” of motions without the need for user input or classified data. Systems that can cluster motion data focus more on “what type of motion data is this, and what is it similar to” rather than which motion is this. The LuminAI project, as described in this paper, is an example of a practical use for motion data clustering that allows the system to respond to user dance moves with a similar but different gesture. To analyze the efficacy and properties of this motion data clustering pipeline, we also propose a novel data visualization tool and the design considerations involved in its development.
dc.description.degree Undergraduate
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/66281
dc.publisher Georgia Institute of Technology
dc.subject unsupervised learning
dc.subject motion capture data
dc.title Analyzing and Learning Movement Through Human-Computer Co-Creative Improvisation and Data Visualization
dc.type Text
dc.type.genre Undergraduate Thesis
dspace.entity.type Publication
local.contributor.advisor Magerko, Brian
local.contributor.corporatename College of Computing
local.contributor.corporatename School of Computer Science
local.contributor.corporatename Undergraduate Research Opportunities Program
local.relation.ispartofseries Undergraduate Research Option Theses
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relation.isOrgUnitOfPublication c8892b3c-8db6-4b7b-a33a-1b67f7db2021
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thesis.degree.level Undergraduate
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