sizigia / stress_in_office

A classification model of physiological measurements of stress response for employees of a small university, based on a study of email-use patterns and interaction with office stressors.

Stress in Office

Stress in Office is a model that classifies physiological measurements of stress response. Trained on a study of email-use patterns and interaction with office stressors, including how these interactions are mediated by individual characteristics, like age, gender, nationality, first language, education.

Table of Contents

1. Project Motivation
2. Installation
3. Repository Breakdown
4. Results
5. Acknowledgements

Project Motivation

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Installation

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Repository Breakdown

• data — data used and produced
  • CSV files:
    • Data to Model: clean data without null values and containing all features available on the dataset
    • Stress vs. Productivity DataFrame: CSV file containing data related to personality tests, physiological measurements and heart rate date from participants
  • Pickles:
    • Study Keys: information on variables (meaning, min and max values, etc) extracted from the paper to support model development
    • Extracted model features: selected features after dimensionality reduction
• notebooks — data treatment process divided into four Jupyter Notebooks with explanations
  • Data Wrangling:
  • Data Cleaning
  • Pre Data Modeling
  • Data Modeling
• presentation — data visualizations and presentation
  • Tableau Workbook
  • First Iteration for Stakeholders

Results

[...]

Acknowledgments

• Office Tasks 2019 – A Multimodal Dataset

  • Contributors: Ioannis Pavlidis, Shaila Zaman, Amanveer Wesley, Christopher Blank
  • Date created: 2019-05-17 06:53 PM | Last Updated: 2019-09-30 08:23 PM
  • Identifier: DOI 10.17605/OSF.IO/ZD2TN
  • Category: Project
  • License: CC0 1.0 Universal

• Stress and productivity patterns of interrupted, synergistic, and antagonistic office activities | Nature - Scientific Data

  • Authors: Shaila Zaman, Amanveer Wesley, Dennis Rodrigo Da Cunha Silva, Pradeep Buddharaju, Fatema Akbar, Ge Gao4, Gloria Mark, Ricardo Gutierrez-Osuna & Ioannis Pavlidis
  • License:

    Open Access
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