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Open-Source Physiological Computing Framework using Heart Rate Variability in Mobile Virtual Reality Applications
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2019 (English)In: 2019 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR): Proceedings, IEEE, 2019Conference paper, Published paper (Refereed)
Abstract [en]

Electronic and mobile health technologies are posed as a tool that can promote self-care and extend coverage to bridge the gap in accessibility to mental care services between low-and high-income communities. However, the current technology-based mental health interventions use systems that are either cumbersome, expensive or require specialized knowledge to be operated. This paper describes the open-source framework PARE-VR, which provides heart rate variability (HRV) analysis to mobile virtual reality (VR) applications. It further outlines the advantages of the presented architecture as an initial step to provide more scalable mental health therapies in comparison to current technical setups; and as an approach with the capability to merge physiological data and artificial intelligence agents to provide computing systems with user understanding and adaptive functionalities. Furthermore, PARE-VR is evaluated with a feasibility study using a specific relaxation exercise with slow-paced breathing. The aim of the study is to get insights of the system performance, its capability to detect HRV metrics in real-time, as well as to identify changes between normal and slow-paced breathing using the HRV data. Preliminary results of the study, with the participation of eleven volunteers, showed high engagement of users towards the VR activity, and demonstrated technical potentialities of the framework to create physiological computing systems using mobile VR and wearable smartwatches for scalable health interventions. Several insights and recommendations were concluded from the study for enhancing the HRV analysis in real-time and conducting future similar studies.

Place, publisher, year, edition, pages
IEEE, 2019.
National Category
Computer Sciences
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-177676DOI: 10.1109/AIVR46125.2019.00027ISBN: 978-1-7281-5605-7 (print)ISBN: 978-1-7281-5604-0 (electronic)OAI: oai:DiVA.org:su-177676DiVA, id: diva2:1383214
Conference
IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR), San Diego, California, USA, 9-11 December, 2019
Available from: 2020-01-07 Created: 2020-01-07 Last updated: 2020-01-21Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Language
  • de-DE
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More languages
Output format
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  • asciidoc
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