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

Knierim, Michael; Pieper, Victor; Schemmer, Max; Loewe, Nico; Reali, Pierluigi

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.


Originalveröffentlichung
DOI: 10.1007/978-3-030-88900-5_11
Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Karlsruhe Service Research Institute (KSRI)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2021
Sprache Englisch
Identifikator ISBN: 978-3-030-88899-2
KITopen-ID: 1000131840
Erschienen in Information Systems and Neuroscience: NeuroIS Retreat 2021. Ed.: F. D. Davis
Veranstaltung NeuroIS Retreat 2021 (2021), Online, 01.06.2021 – 03.06.2021
Verlag Springer
Seiten 107-117
Vorab online veröffentlicht am 26.04.2021
Schlagwörter Flow Experience, Field Study, ECG, LASSO, Random Forest
Nachgewiesen in Dimensions
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