File:Retrieving information from complex data-HniC6Szdgi0.webm
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[edit]DescriptionRetrieving information from complex data-HniC6Szdgi0.webm |
English: The webinar "Retrieving information from complex data: classifying Parkinson-related variables using the Goal Keeper Game” was given by Rafael Stern, from Universidade Federal de São Carlos...In this presentation, he discusses how to extract information from the GG that is useful for predicting gait performance. People with Parkinson's disease (PD) display poorer gait performance when walking under complex conditions than under simple conditions. Screening tests that evaluate gait performance changes under complex walking conditions may be valuable tools for early intervention, especially if allowing for massive data collection. The Goalkeeper Game (GG) might allow such a massive collection...Find other details about this seminar and the others in the series here: neuromat.numec.prp.usp.br/content/nmregsem |
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Source | YouTube: Retrieving information from complex data – View/save archived versions on archive.org and archive.today |
Author | NeuroMat |
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[edit]This media was produced by NeuroMat and was licensed as Creative Commons BY-SA 4.0. The Research, Innovation and Dissemination Center for Neuromathematics (RIDC NeuroMat) is a Brazilian research center hosted by the University of São Paulo and funded by the São Paulo Research Foundation (FAPESP).
Attribution in English: RIDC NeuroMat Attribution in Portuguese: CEPID NeuroMat |
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current | 06:32, 26 October 2020 | 1 h 11 min 24 s, 1,280 × 720 (97.19 MB) | Carybe (talk | contribs) | =={{int:filedesc}}== {{Information |description={{pt|1=The webinar "Retrieving information from complex data: classifying Parkinson-related variables using the Goal Keeper Game” was given by Rafael Stern, from Universidade Federal de São Carlos...In this presentation, he discusses how to extract information from the GG that is useful for predicting gait performance. People with Parkinson's disease (PD) display poorer gait performance when walking under complex conditions than under simple co... |
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Short title | Retrieving information from complex data |
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Author | NeuroMat |
User comments | The webinar "Retrieving information from complex data: classifying Parkinson-related variables using the Goal Keeper Game” was given by Rafael Stern, from Universidade Federal de São Carlos.
In this presentation, he discusses how to extract information from the GG that is useful for predicting gait performance. People with Parkinson's disease (PD) display poorer gait performance when walking under complex conditions than under simple conditions. Screening tests that evaluate gait performance changes under complex walking conditions may be valuable tools for early intervention, especially if allowing for massive data collection. The Goalkeeper Game (GG) might allow such a massive collection. Find other details about this seminar and the others in the series here: neuromat.numec.prp.usp.br/content/nmregsem |
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