Low-power Machine Learning for Visitor Engagement in Museums

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.