When no one is watching: Ecological momentary assessment to understand situated social robot use in healthcare

Abstract

Socially-Assistive Robots (SARs) hold great potential to revolutionize the way we manage chronic illness outside clinical settings, but a current limitation to their broad adoption for this purpose is the lack of "ground truth"around interactions between robots and humans in in-home settings. Such ground truth is a necessity for using robotic sensor data for machine learning models of patient activity patterns or to create AI to customize robotic interactive behavior autonomously. Traditional subjective recall-based data collection methods lack the fine-grained temporal detail to support such AI development, as well as suffering from "recall bias"effects. One potential solution to this challenge is to adapt novel forms of interaction assessment, such as ecological momentary assessment (EMA), to collect patient interaction data in real-Time. Here we describe a pilot study utilizing such an EMA system with SARs. We describe the development of the EMA framework, theoretical design issues, and lessons learned. Preliminary machine learning results indicate 75-80% accuracy for detecting specific interaction modalities. We also discuss the potential utility of EMA for exploring cross-cultural differences with in-The-wild robot use, and as a tool to support participatory design research on robotics in healthcare settings.