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Beyond Skin-Deep Investigations of Epidermal Activity to Predict Mental Workload Using Multiple Phasic Components: Implications for Real-Time Analysis Using Affordable Wearables

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HCI International 2022 Posters (HCII 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1581))

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Abstract

Epidermal Activity (EDA) has proved difficult for use in predicting workload states in complex tasks. Furthermore, a reliance on a single phasic component of the EDA may mask the true effects of workload on EDA. We hypothesized that decomposing a single time series into multiple principal components would identify a variety of phasic components reflecting the effects of task workload. In a 30-min simulated flight, pilots flew in two workload conditions which varied by task complexity. Mixed-factor ANOVA was used to analyze workload (within-subjects) and age (between-group) effects on the phasic components. The largest phasic component showed a marginal effect of workload, where higher workload was associated with larger EDA. Likewise, the smallest phasic component showed a significant main effect of workload, where higher workload was associated with larger EDA. Additionally, there was a significant effect of age, such that older pilots demonstrated larger EDA as compared to younger pilots. Results show that it is advantageous to decompose EDA time series data into multiple components of phasic data when predicting workload. Further work should be done to increase our understanding of using principal components of EDA in predicting workload during complex tasks.

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Authors and Affiliations

  1. Carleton University, Ottawa, ON, K1S 5B6, Canada

    Simran Sian, Anya D. Pejemsky, Kathleen Van Benthem & Chris M. Herdman

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  1. Simran Sian
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  2. Anya D. Pejemsky
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  3. Kathleen Van Benthem
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  4. Chris M. Herdman
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Correspondence to Simran Sian .

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Editors and Affiliations

  1. University of Crete and Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Constantine Stephanidis

  2. Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Margherita Antona

  3. Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Stavroula Ntoa

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Sian, S., Pejemsky, A.D., Van Benthem, K., Herdman, C.M. (2022). Beyond Skin-Deep Investigations of Epidermal Activity to Predict Mental Workload Using Multiple Phasic Components: Implications for Real-Time Analysis Using Affordable Wearables. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2022 Posters. HCII 2022. Communications in Computer and Information Science, vol 1581. Springer, Cham. https://doi.org/10.1007/978-3-031-06388-6_30

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  • DOI: https://doi.org/10.1007/978-3-031-06388-6_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06387-9

  • Online ISBN: 978-3-031-06388-6

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