Low-power Machine Learning (ML) technologies that process data locally on consumer-level hardware are well suited for interactive applications, however, their potential for audience engagement in museums is largely unexplored. This paper presents a case study using lightweight ML models for human pose estimation and gesture classification to enable visitors' engagement with interactive projections of interior designs. An empirical evaluation found the application is highly engaging and motivates visitors to learn more about the designs. Uncertainty in ML predictions, experienced as tracking inaccuracies, jitter, or gesture recognition problems, have little impact on their positive user experience. The findings warrant future research to explore the potential of low-power ML for visitor engagement in other use cases and heritage contexts.

This paper reports findings from expert interviews discussing in-gallery commenting systems with museum professionals. Its main contribution is an exploration of museum perspectives on critical aspects of commenting platforms including content moderation, comment metadata, access and openness, ownership and reuse of comments, backend requirements, deployment and maintenance. The paper relates findings to system requirements and flags up a number of design tensions between visitors' attention to exhibits and their engagement with interpretive resources; visitors' communication behaviours and their contemplative needs; museums' requirements for content moderation and visitors' user experience when submitting comments. The findings will be useful to researchers and practitioners developing in-gallery commenting systems and other platforms collecting and displaying visitor comments in museums.

Being able to quantify the attention potential of pervasive display placements holds promise in selecting suitable placements, scoping expectations of impact, informing display designs and calibrating engagement data against placement-related factors when evaluating display designs. This paper contributes a first version of an instrument to quantify the attention potential of display placements, focusing in particular on small interactive displays in museum environments. It reports on an empirical evaluation revealing strong and significant correlations between quantified attention potential and measured attention and engagement. The paper describes the methodology of the evaluation, discusses its findings and their limitations, and concludes with a call for more research into quantifying the attention potential of display placements.