The Impact of Synthetic Data on Fall Detection Application

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dc.contributor.authorNgu, Anne H. H.
dc.contributor.authorDebnath, Minakshi
dc.date.accessioned2024-04-11T18:53:23Z
dc.date.available2024-04-11T18:53:23Z
dc.date.issued2024-03
dc.description.abstractThe accurate recognition of the dynamic of fall using deep learning requires a lot of data. Three different methods for creating realistic synthetic fall data utilizing generative AI with diffusion, fall data extraction from 2D video recordings, and traditional data augmentation techniques are explored.
dc.description.departmentComputer Science
dc.formatImage
dc.format.extent1 page
dc.format.medium1 file (.pdf)
dc.identifier.citationNgu, A. H. H., & Debnath, M. (2024). The impact of synthetic data on fall detection application. Poster presented at the Health Scholar Showcase, Translational Health Research Center, San Marcos, Texas.
dc.identifier.urihttps://hdl.handle.net/10877/18439
dc.language.isoen
dc.sourceHealth Scholar Showcase, 2024, Texas State University Translational Health Science Center, San Marcos, Texas, United States.
dc.subjectfall detection
dc.subjectsynthetic data
dc.titleThe Impact of Synthetic Data on Fall Detection Application
dc.typePoster

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