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Remote Collection of Physiological Data in a Virtual Reality Study.

Gnacek, M., Seiss, E., Kostoulas, T., Balaguer-Ballester, E., Mavridou, I. and Nduka, C., 2021. Remote Collection of Physiological Data in a Virtual Reality Study. In: CHI 21, 15-16 May 2021, XR Remote Research Workshop.

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Official URL: https://chi2021.acm.org/

Abstract

Recent pandemic related events have effectively put a stop to most in-lab data collection which has a profound negative impact on many research fields. Online and remote data collection, without the need to travel to a laboratory, starts to be used as a valuable alternative in some scenarios. This approach does not only help to resume some research activities, it also has an enormous potential to change how research is conducted in future. With the use of our biometric sensing system for Virtual Reality (emteqGO), we designed a VR experience autonomously guiding participants through the study. The combination of hardware posted to participants, alongside software solutions handling the setup, data collection, quality assurance and upload for immediate access enables a fully remote, unsupervised approach to data collection. While this approach might be the only feasible solution for some researchers, it has also laid the groundwork for possible future direction of research where remote data collection isa new way to enhance access to participants who typically would not travel to the laboratories. In designing these solutions, we found that for unsupervised remote data collection to work effectively, setup procedures must be easy to follow to obtain high quality data and the entire process must be highly robust, reliable, and built with a high degree of redundancy. Post-pandemic, there are many benefits of an ongoing use of remote research paradigms. These include ameliorating the diversity problem afflicting current research by widening the participant pool, improved research quality by collecting data in more naturalistic environments, and improving protocol standardisation using virtual reality.

Item Type:Conference or Workshop Item (Speech)
Uncontrolled Keywords:remote data collection, virtual reality, XR, biometrics, EMG, PPG, IMU
Group:Faculty of Science & Technology
ID Code:35799
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:20 Jul 2021 12:55
Last Modified:15 Aug 2021 08:30

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