Activity Recognition Model for Patients Data Stream using Adaptive Random Forest and Deep Learning Techniques

Hayder, K. Fatlawi ✉ [Hayder, Fatlawi, author] ELTE IK PhD Informatika Doktori Iskola (ELTE / ELU FoI); Attla, Kiss [Kiss, Attila (Számítástudomány), author] ELTE IK Department of Information Systems (ELTE / ELU FoI / ICS)

English Scientific Conference paper (Conference paper)
    Precise detection of the current activity status for chronic diseases patients could play a significant role for protect their lives against sudden decline in health. Combining the information form various data resources present a reasonable challenge.On the other hand, stream classification techniques have a privilege of low computational time but they need a feedback for adapting the classifier. This work proposes a model for providing efficient automatic feedback for adaptive random forest classifier using deep learning classifying of video stream from surveillance systems.
    Citation styles: IEEEACMAPAChicagoHarvardCSLCopyPrint
    2021-12-05 20:38