Distributed Remote Psychophysiological Data Collection for UX Evaluation: A Pilot Project

User experience (UX) research has been critically impacted by the recent COVID-19 pandemic and the sanitary restrictions put in place. Observational or perceptual studies can be adapted remotely with participants using their own computer and internet access. However, studies based on the unconscious and automatic physiological states of participants use neurophysiological measurements that requires highly specific hardware. Electrodermal activity (EDA) or electrocardiogram (ECG) based studies are complex to transpose to a remote environment since researchers have no physical contact with the participants. To address this concern, our research team previously developed a remote instrument that can collect the EDA and the ECG activity at the participants' location through a moderated self-installation of sensors. We developed a protocol for remote physiological data collection that we pilot tested with 2 UX studies. After each study, we administered an open-ended questionnaire regarding the full experience of remote data-collection from both the moderator's and the participant's side. We collected 92 responses total which provided us with a rich dataset that we analyzed through a thematic analysis lens in order to uncover the success factors of remote psychophysiological data collection. Operational support, moderator-participant collaboration, individual characteristics, and technological capabilities clearly emerged as drivers for success. This project aimed to develop a rigorous and contextually relevant protocol for remote physiological data collection in UX evaluations, train our research team on the developed protocol, and provide guidance regarding remote physiological data collection activities.

Guidelines for Collecting Automatic Facial Expression Detection Data Synchronized with a Dynamic Stimulus in Remote Moderated User Tests

Because of the COVID-19 pandemic, telework policies have required many user experience (UX) labs to restrict their research activities to remote user testing. Automatic Facial Expression Analysis (AFEA) is an accessible psychophysiological measurement that can be easily implemented in remote user tests. However, to date, the literature on Human Computer Interaction (HCI) has provided no guidelines for remote moderated user tests that collect facial expression data and synchronize them with the state of a dynamic stimulus such as a webpage. To address this research gap, this article offers guidelines for effective AFEA data collection that are based on a methodology developed in a concrete research context and on the lessons learned from applying it in four remote moderated user testing projects. Since researchers have less control over test environment settings, we maintain that they should pay greater attention to factors that can affect face detection and\or emotion classification prior, during, and after remote moderated user tests. Our study contributes to the development of methods for including psychophysiological and neurophysiological measurements in remote user tests that offer promising opportunities for information systems (IS) research, UX design, and even digital health research. © 2021, Springer Nature Switzerland AG.