Digital tabletops present the opportunity to combine the social advantages of traditional tabletop games with the automation and streamlined gameplay of video games. However, it is unclear whether the addition of automation enhances or detracts from the game experience. A study was performed where groups played three versions of the cooperative board game Pandemic, with varying degrees of automation. The study revealed that while game automation can provide advantages to players, it can also negatively impact enjoyment, game state awareness, and flexibility in game play.
Fast and accurate touch detection is critical to the usability of multi-touch tabletops. In optical tabletops, such as those using the popular FTIR and DI technologies, this requires efficient and effective noise reduction to enhance touches in the camera's input. Common approaches to noise reduction do not scale to larger tables, leaving designers with a choice between accuracy problems and expensive hardware. In this paper, we present a novel noise reduction algorithm that provides better touch recognition than current alternatives, particularly in noisy environments, without imposing higher computational cost. We empirically compare our algorithm to other noise reduction approaches using data collected from tabletops at research labs in Canada and Europe.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.