BioFidget: biofeedback for respiration training using an augmented fidget spinner

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Abstract

This paper presents BioFidget, a biofeedback system that integrates physiological sensing and display into a smart fidget spinner for respiration training. We present a simple yet novel hardware design that transforms a fidget spinner into 1) a nonintrusive heart rate variability (HRV) sensor, 2) an electromechanical respiration sensor, and 3) an information display. The combination of these features enables users to engage in respiration training through designed tangible and embodied interactions, without requiring them to wear additional physiological sensors. The results of this empirical user study prove that the respiration training method reduces stress, and the proposed system meets the requirements of sensing validity and engagement with 32 participants in a practical setting.

Original languageEnglish
Title of host publicationProceedings of the 2018 CHI Conference on Human Factors in Computing Systems
Subtitle of host publication(CHI '18)
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery, Inc
Number of pages12
ISBN (Print)9781450356206
DOIs
Publication statusPublished - 20 Apr 2018
Event2018 CHI Conference on Human Factors in Computing Systems, CHI 2018 - Montreal, Canada, Montreal, Canada
Duration: 21 Apr 201826 Apr 2018
Conference number: 36
http://chi2018.acm.org
https://chi2018.acm.org/

Conference

Conference2018 CHI Conference on Human Factors in Computing Systems, CHI 2018
Abbreviated titleCHI '18
CountryCanada
CityMontreal
Period21/04/1826/04/18
Internet address

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Keywords

  • Biofeedback
  • Fidget spinner
  • Physiological sensing
  • Respiration training
  • Stress
  • Tangible interaction

Cite this

Liang, R. H., Yu, B., Xue, M., Hu, J., & Feijs, L. M. G. (2018). BioFidget: biofeedback for respiration training using an augmented fidget spinner. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems: (CHI '18) [613] New York, NY, USA: Association for Computing Machinery, Inc. https://doi.org/10.1145/3173574.3174187