Predicting In-Field Flow Experiences Over Two Weeks from ECG Data: A Case Study

Michael T. Knierim (), Victor Pieper (), Max Schemmer (), Nico Loewe () and Pierluigi Reali ()
Additional contact information
Michael T. Knierim: Karlsruhe Institute of Technology (KIT)
Victor Pieper: Karlsruhe Institute of Technology (KIT)
Max Schemmer: Karlsruhe Institute of Technology (KIT)
Nico Loewe: Karlsruhe Institute of Technology (KIT)
Pierluigi Reali: Politecnico di Milano

A chapter in Information Systems and Neuroscience, 2021, pp 96-102 from Springer

Abstract: Abstract Predicting flow intensities from unobtrusively collected sensor data is considered an important yet challenging endeavor for NeuroIS scholars aiming to understand and support flow during IS use. In this direction, a limitation has been the focus on cross-subject models built on data collected in controlled laboratory settings. We investigate the potential of predicting flow in the field through personalized models by collecting report and ECG data from a clerical worker over the course of two weeks. Results indicate that a lack of variation in flow experiences during this time likely diminished these potentials. Through pre-training feature selection methods, model accuracies could be achieved that nonetheless approach related cross-subject flow prediction work. Novel recommendations are developed that could introduce more flow variation in future flow field studies to further investigate the within-subject predictability of flow based on wearable physiological sensor data.

Keywords: Flow experience; Field study; ECG; LASSO; Random forest (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-030-88900-5_11

Ordering information: This item can be ordered from
http://www.springer.com/9783030889005

DOI: 10.1007/978-3-030-88900-5_11

Access Statistics for this chapter

More chapters in Lecture Notes in Information Systems and Organization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().