Dataset of perceptions of waiting in a virtual reality (VR) doctor's office receptionist queue with and without notifications on the reason for a delay

In this paper we present our virtual reality dataset for understanding how participants perceive waiting in a queue to confirm an appointment at a virtual doctor’s office. Our data studies two scenarios, namely when the participant has been provided a reason on why there is a delay by a virtual receptionist and when they have not been notified of the reason for the delay. In our study, the reason for the delay is an issue with the virtual receptionist’s computer system. Our dataset consists of 30 participants interacting in the virtual doctor’s office using a Meta Quest Pro. We designed the virtual doctor’s office to represent the typical scene from a real world setting where a patient has to wait in queue before checking in for their appointment with the receptionist. The participant is placed in a queue with one other patient, in this case a virtual non-playable character, and must interact with a receptionist, also a virtual non-playable character. Each participant completes both scenarios, or treatments, with the order of treatments being assigned at random. To the best of our knowledge, our dataset is the first dataset for studying how people perceive delays when waiting to receive service in an immersive virtual environment. Participants provided data in a single 1-hour long session. Each participant completed a demographics survey consisting of their age, ethnicity, race, self-identified gender, education level, glasses and/or contact use, experience with video games, experience with VR, and experience with telehealth services. Prior to immersion participants completed the Frustration Discomfort Scale (FDS) to understand frustration tolerance levels. After each treatment immersion participants answered questions on their perception of the length of delay, their frustration level on a 5-point Likert scale, and their likelihood to exit the simulation, also on a 5-point Likert scale. After both treatments, we administered the FDS again and obtained system usability using the System Usability Scale, task load using NASA Task Load Index, and cybersickness using the Virtual Reality Sickness Questionnaire. Additionally, for each participant we provide head, left-hand, and right-hand position and orientation data as well as eye-gaze data consisting of the eye gaze hit location and a human readable object name for the scene element being observed. Since our dataset is collected in a single virtual clinic with participants coming from a single university, it can be used as an exploratory resource for understanding participant behavior and to design larger scale studies and collections. The provided dataset enables research in understanding how participant frustration levels, likelihood to exit, or perception of time changes when they are provided knowledge of the reason for the delay as opposed to when knowledge is absent. The dataset allows researchers to use the eye gaze, head, and hand movement data to understand how participants engage during idle times associated with waiting. The dataset can enable the design of AI algorithms for predicting participant frustration levels, likelihood of exit, and knowledge of why there is a delay based on head, hands, and eye gaze movement. The dataset can enable the development of interruption management systems for immersive applications that provide user engagement based on delays to reduce frustration. Finally, the dataset can be used for behavior-based security measures for protecting critical applications, such as VR-based telehealth applications.