research-article

Designing Implicit Interfaces for Physiological Computing: Guidelines and Lessons Learned Using fNIRS

Authors:

Erin Treacy Solovey,

Daniel Afergan,

Samuel W. Hincks,

Robert J. K. JacobAuthors Info & Claims

ACM Transactions on Computer-Human Interaction (TOCHI), Volume 21, Issue 6

Article No.: 35, Pages 1 - 27

https://doi.org/10.1145/2687926

Published: 14 January 2015 Publication History

Abstract

A growing body of recent work has shown the feasibility of brain and body sensors as input to interactive systems. However, the interaction techniques and design decisions for their effective use are not well defined. We present a conceptual framework for considering implicit input from the brain, along with design principles and patterns we have developed from our work. We also describe a series of controlled, offline studies that lay the foundation for our work with functional near-infrared spectroscopy (fNIRS) neuroimaging, as well as our real-time platform that serves as a testbed for exploring brain-based adaptive interaction techniques. Finally, we present case studies illustrating the principles and patterns for effective use of brain data in human--computer interaction. We focus on signals coming from the brain, but these principles apply broadly to other sensor data and in domains such as aviation, education, medicine, driving, and anything involving multitasking or varying cognitive workload.

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Index Terms

Designing Implicit Interfaces for Physiological Computing: Guidelines and Lessons Learned Using fNIRS
1. Human-centered computing
  1. Human computer interaction (HCI)

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cover image ACM Transactions on Computer-Human Interaction

ACM Transactions on Computer-Human Interaction Volume 21, Issue 6

Special Issue on Physiological Computing for Human-Computer Interaction

January 2015

144 pages

ISSN:1073-0516

EISSN:1557-7325

DOI:10.1145/2722827

Editor:
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Publication History

Published: 14 January 2015

Accepted: 01 October 2014

Revised: 01 October 2014

Received: 01 January 2014

Published in TOCHI Volume 21, Issue 6

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Cited By

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Chiossi FStepanova ETag BPerusquia-Hernandez MKitson ADey AMayer SEl Ali A(2024)PhysioCHI: Towards Best Practices for Integrating Physiological Signals in HCIExtended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3636286(1-7)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613905.3636286
Ruotsalo TMäkelä KSpapé M(2024)Crowdsourcing Affective Annotations Via fNIRS-BCIIEEE Transactions on Affective Computing10.1109/TAFFC.2023.327391615:1(297-308)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TAFFC.2023.3273916
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Kosch TKarolus JZagermann JReiterer HSchmidt AWoźniak P(2023)A Survey on Measuring Cognitive Workload in Human-Computer InteractionACM Computing Surveys10.1145/358227255:13s(1-39)Online publication date: 13-Jul-2023
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Hirshfield LWickens CDoherty ESpencer CWilliams THayne L(2023)Toward Workload-Based Adaptive Automation: The Utility of fNIRS for Measuring Load in Multiple Resources in the BrainInternational Journal of Human–Computer Interaction10.1080/10447318.2023.226624240:22(7404-7430)Online publication date: 23-Oct-2023
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