"Machine Learning Classification Of Stress Intervention Following Cognitive Load Tasks" - Information and Links:

Machine Learning Classification Of Stress Intervention Following Cognitive Load Tasks - Info and Reading Options


“Machine Learning Classification Of Stress Intervention Following Cognitive Load Tasks” Metadata:

  • Title: ➤  Machine Learning Classification Of Stress Intervention Following Cognitive Load Tasks
  • Authors:

Edition Identifiers:

  • Internet Archive ID: osf-registrations-5k9ra-v1

AI-generated Review of “Machine Learning Classification Of Stress Intervention Following Cognitive Load Tasks”:


"Machine Learning Classification Of Stress Intervention Following Cognitive Load Tasks" Description:

The Internet Archive:

Overview In Thayer & Stevens (2021), we conducted two experiments investigating the effects of human-animal interaction (HAI) on affect and cognition. In these experiments, participants experienced cognitive (working memory, attentional control) tasks before and after either a three-minute exposure to a dog (HAI condition) or a control task. Self-report measures of affect (mood, anxiety, stress) were collected repeatedly throughout the sessions. While interacting with a dog influenced measures of affect, they did not influence cognition. In addition to the affect and cognition measures, participants wore an Empatica E4 to record heart rate, electrodermal activity, body temperature, etc. However, we did not analyze these data, as the sampling rate was too low for robust measures of heart rate variability (Malik et al. 1996, Laborde et al. 2017). Arce & Gehringer (2021) have investigated machine learning algorithms that take Empatica E4 data and classify whether participants were experiencing stress induction. This offers the opportunity to apply the machine learning algorithms developed to classify stress to the Thayer & Stevens (2021) data and address two specific aims: 1. Assess the predictive accuracy of the machine learning algorithms in classifying intervention exposure 2. Determine which features are most important in classifying exposure Research questions 1. How accurately can machine learning algorithms classify stress intervention exposure? 2. Which algorithms best predict stress intervention exposure? 3. Does the accelerometer data act as a naive feature that gives extra weighting to the different affect states? 4. Which features best predict passive and active stress intervention exposures?

Read “Machine Learning Classification Of Stress Intervention Following Cognitive Load Tasks”:

Read “Machine Learning Classification Of Stress Intervention Following Cognitive Load Tasks” by choosing from the options below.

Available Downloads for “Machine Learning Classification Of Stress Intervention Following Cognitive Load Tasks”:

"Machine Learning Classification Of Stress Intervention Following Cognitive Load Tasks" is available for download from The Internet Archive in "data" format, the size of the file-s is: 0.10 Mbs, and the file-s went public at Fri Apr 15 2022.

Legal and Safety Notes

Copyright Disclaimer and Liability Limitation:

A. Automated Content Display
The creation of this page is fully automated. All data, including text, images, and links, is displayed exactly as received from its original source, without any modification, alteration, or verification. We do not claim ownership of, nor assume any responsibility for, the accuracy or legality of this content.

B. Liability Disclaimer for External Content
The files provided below are solely the responsibility of their respective originators. We disclaim any and all liability, whether direct or indirect, for the content, accuracy, legality, or any other aspect of these files. By using this website, you acknowledge that we have no control over, nor endorse, the content hosted by external sources.

C. Inquiries and Disputes
For any inquiries, concerns, or issues related to the content displayed, including potential copyright claims, please contact the original source or provider of the files directly. We are not responsible for resolving any content-related disputes or claims of intellectual property infringement.

D. No Copyright Ownership
We do not claim ownership of any intellectual property contained in the files or data displayed on this website. All copyrights, trademarks, and other intellectual property rights remain the sole property of their respective owners. If you believe that content displayed on this website infringes upon your intellectual property rights, please contact the original content provider directly.

E. Fair Use Notice
Some content displayed on this website may fall under the "fair use" provisions of copyright law for purposes such as commentary, criticism, news reporting, research, or educational purposes. If you believe any content violates fair use guidelines, please reach out directly to the original source of the content for resolution.

Virus Scanning for Your Peace of Mind:

The files provided below have already been scanned for viruses by their original source. However, if you’d like to double-check before downloading, you can easily scan them yourself using the following steps:

How to scan a direct download link for viruses:

  • 1- Copy the direct link to the file you want to download (don’t open it yet).
    • (a free online tool) and paste the direct link into the provided field to start the scan.
  • 2- Visit VirusTotal (a free online tool) and paste the direct link into the provided field to start the scan.
  • 3- VirusTotal will scan the file using multiple antivirus vendors to detect any potential threats.
  • 4- Once the scan confirms the file is safe, you can proceed to download it with confidence and enjoy your content.

Available Downloads

  • Source: Internet Archive
  • All Files are Available: Yes
  • Number of Files: 5
  • Number of Available Files: 5
  • Added Date: 2022-04-15 06:00:26
  • Scanner: Internet Archive Python library 1.9.9

Available Files:

1- ZIP

  • File origin: original
  • File Format: ZIP
  • File Size: 0.00 Mbs
  • File Name: bag.zip
  • Direct Link: Click here

2- Metadata

  • File origin: original
  • File Format: Metadata
  • File Size: 0.00 Mbs
  • File Name: osf-registrations-5k9ra-v1_files.xml
  • Direct Link: Click here

3- Metadata

  • File origin: original
  • File Format: Metadata
  • File Size: 0.00 Mbs
  • File Name: osf-registrations-5k9ra-v1_meta.sqlite
  • Direct Link: Click here

4- Metadata

  • File origin: original
  • File Format: Metadata
  • File Size: 0.00 Mbs
  • File Name: osf-registrations-5k9ra-v1_meta.xml
  • Direct Link: Click here

5- Archive BitTorrent

  • File origin: metadata
  • File Format: Archive BitTorrent
  • File Size: 0.00 Mbs
  • File Name: osf-registrations-5k9ra-v1_archive.torrent
  • Direct Link: Click here

Search for “Machine Learning Classification Of Stress Intervention Following Cognitive Load Tasks” downloads:

Visit our Downloads Search page to see if downloads are available.

Find “Machine Learning Classification Of Stress Intervention Following Cognitive Load Tasks” in Libraries Near You:

Read or borrow “Machine Learning Classification Of Stress Intervention Following Cognitive Load Tasks” from your local library.

Buy “Machine Learning Classification Of Stress Intervention Following Cognitive Load Tasks” online:

Shop for “Machine Learning Classification Of Stress Intervention Following Cognitive Load Tasks” on popular online marketplaces.



Find "Machine Learning Classification Of Stress Intervention Following Cognitive Load Tasks" in Wikipdedia