File:Retrieving information from complex data-HniC6Szdgi0.webm
Size of this JPG preview of this WEBM file: 800 × 450 pixels. Other resolutions: 320 × 180 pixels | 640 × 360 pixels | 1,280 × 720 pixels.
Original file (WebM audio/video file, VP9/Opus, length 1 h 11 min 24 s, 1,280 × 720 pixels, 190 kbps overall, file size: 97.19 MB)
Captions
Captions
Add a one-line explanation of what this file represents
Summary edit
| Description |
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 |
| Date | |
| Source | YouTube: Retrieving information from complex data – View/save archived versions on archive.org and archive.today |
| Author | NeuroMat |
Licensing 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 |
This file is licensed under the Creative Commons Attribution-Share Alike 4.0 International license.
Attribution: RIDC NeuroMat
- You are free:
- to share – to copy, distribute and transmit the work
- to remix – to adapt the work
- Under the following conditions:
- attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- share alike – If you remix, transform, or build upon the material, you must distribute your contributions under the same or compatible license as the original.
|
This file, which was originally posted to YouTube: Retrieving information from complex data, was reviewed on 14 November 2020 by the automatic software YouTubeReviewBot, which confirmed that this video was available there under the stated Creative Commons license on that date. This file should not be deleted if the license has changed in the meantime. The Creative Commons license is irrevocable.
The bot only checks for the license, human review is still required to check if the video is a derivative work, has freedom of panorama related issues and other copyright problems that might be present in the video. Visit licensing for more information. If you are a license reviewer, you can review this file by manually appending |
|
File history
Click on a date/time to view the file as it appeared at that time.
| Date/Time | Thumbnail | Dimensions | User | Comment | |
|---|---|---|---|---|---|
| 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... |
You cannot overwrite this file.
File usage on Commons
The following page uses this file: