Towards estimating affective states in Virtual Reality based on behavioral data

Abstract

Inferring users’ perceptions of Virtual Environments (VEs) is essential for Virtual Reality (VR) research. Traditionally, this is achieved through assessing users’ affective states before and after being exposed to a VE, based on standardized, self-assessment questionnaires. The main disadvantage of questionnaires is their sequential administration, i.e., a user’s affective state is measured asynchronously to its generation within the VE. A synchronous measurement of users’ affective states would be highly favorable, e.g., in the context of adaptive systems. Drawing from nonverbal behavior research, we argue that behavioral measures could be a powerful approach to assess users’ affective states in VR. In this paper, we contribute by providing methods and measures evaluated in a user study involving 42 participants to assess a users’ affective states by measuring head movements during VR exposure. We show that head yaw significantly correlates with presence, mental and physical demand, perceived performance, and system usability. We also exploit the identified relationships for two practical tasks that are based on head yaw: (1) predicting a user’s affective state, and (2) detecting manipulated questionnaire answers, i.e., answers that are possibly non-truthful. We found that affective states can be predicted significantly better than a naive estimate for mental demand, physical demand, perceived performance, and usability. Further, manipulated or non-truthful answers can also be estimated significantly better than by a naive approach. These findings mark an initial step in the development of novel methods to assess user perception of VEs.