Conceptualization evaluates measurement uncertainty in XR HMDs, suggesting potential for enhanced measurement systems.
This paper aims to conceptualize eXtended Reality Head-Mounted Displays (XR HMDs) as measuring systems. In fact, XR HMDs are commonly employed to integrate the physical and digital worlds by overlaying digital information onto the surrounding environment or enabling interactions with it, thereby providing immersive user experiences. When information originates from environment-related physical quantities, it is typically acquired through external measuring systems, with XR HMDs acting merely as a visualization device. However, the availability of advanced onboard sensors and computational units has opened the possibility of reshaping the role of XR HMDs in the measurement processes. In this regard, much of the existing literature focuses on measurement algorithms embedded within XR HMDs; on the other hand, limited attention has been dedicated to performance aspects from a metrological perspective, which are instead crucial for a comprehensive conceptualization of XR HMDs as proper measuring devices. Within this novel framework, a key distinguishing factor is that users wearing XR HMDs (who act as measurement operators ), introduce additional metrological challenges, particularly concerning their impact on the overall measurement uncertainty, which are generally more straightforward to manage in conventional measurement scenarios. To illustrate this perspective, without losing generality, a case study on distance measurement using a commercial XR HMD is presented. The results demonstrate the potential of XR HMDs to serve as proper measuring systems, provided that their specific sources of measurement uncertainty are adequately identified, evaluated, and combined. • Conceptualization of XR head-mounted displays as measuring systems. • User influence identified as a key contributor to measurement uncertainty. • GUM-compliant measurement uncertainty evaluation. • A case study on distance measurement validates XR HMDs for traceable measurement applications.
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Angrisani et al. (2026) studied this question.
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