Building Context-Aware Digital Health Tools: A Framework for Evaluating Real-World Human Health and Behavior

Abstract

Digital health tools that utilize innovative technologies (wearable / portable devices, human-centric artificial intelligence / machine learning [AI / ML], etc.) have enormous potential for targeted human performance monitoring, pain, disease, and/or disability management. Efforts in this space have, for the most part, yet to translate into clinically useful applications. For every potential opportunity that these tools present, challenges persist, including how to devise experimental protocols in unconstrained, real-world settings to ensure that meaningful data is captured, how to make sense of those data / reconcile with what we observe in the laboratory, and how best to integrate these insights within the clinical ecosystem. In this presentation, I discuss the generalized framework our team (EPIC Laboratory) has been developing that supports proactive planning for, anticipation of, and adaptation to real-world human behavior, with a specific emphasis on the principles of human movement and cognitive science, contextual design, and user experience evaluation as applied to engineering design and product development.