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Visual Analytics of Work Behavior Data - Insights on Individual Differences

Koldijk, Saskia and Bernard, Jürgen and Ruppert, Tobias and Kohlhammer, Jörn and Neerincx, Mark A. and Kraaij, Wessel (2015):
Visual Analytics of Work Behavior Data - Insights on Individual Differences.
Eurographics Association, Goslar, In: Eurographics Conference on Visualization (EuroVis), DOI: 10.2312/eurovisshort.20151129,
[Conference or Workshop Item]

Abstract

Stress in working environments is a recent concern. We see potential in collecting sensor data to detect patterns in work behavior with potential danger to well-being. In this paper, we describe how we applied visual analytics to a work behavior dataset, containing information on facial expressions, postures, computer interactions, physiology and subjective experience. The challenge is to interpret this multi-modal low level sensor data. In this work, we alternate between automatic analysis procedures and data visualization. Our aim is twofold: 1) to research the relations of various sensor features with (stress related) mental states, and 2) to develop suitable visualization methods for insight into a large amount of behavioral data. Our most important insight is that people differ a lot in their (stress related) work behavior, which has to be taken into account in the analyses and visualizations.

Item Type: Conference or Workshop Item
Erschienen: 2015
Creators: Koldijk, Saskia and Bernard, Jürgen and Ruppert, Tobias and Kohlhammer, Jörn and Neerincx, Mark A. and Kraaij, Wessel
Title: Visual Analytics of Work Behavior Data - Insights on Individual Differences
Language: English
Abstract:

Stress in working environments is a recent concern. We see potential in collecting sensor data to detect patterns in work behavior with potential danger to well-being. In this paper, we describe how we applied visual analytics to a work behavior dataset, containing information on facial expressions, postures, computer interactions, physiology and subjective experience. The challenge is to interpret this multi-modal low level sensor data. In this work, we alternate between automatic analysis procedures and data visualization. Our aim is twofold: 1) to research the relations of various sensor features with (stress related) mental states, and 2) to develop suitable visualization methods for insight into a large amount of behavioral data. Our most important insight is that people differ a lot in their (stress related) work behavior, which has to be taken into account in the analyses and visualizations.

Publisher: Eurographics Association, Goslar
Uncontrolled Keywords: Business Field: Digital society, Research Area: Modeling (MOD), Pattern recognition, Signal processing, Data analysis, Data visualization
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: Eurographics Conference on Visualization (EuroVis)
Date Deposited: 08 May 2019 07:53
DOI: 10.2312/eurovisshort.20151129
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