Discovering Behavioral Patterns Using Conversational Technology for In-Home Health and Well-Being Monitoring

Advancements in conversational artificial intelligence (AI) have created unparalleled opportunities to promote the independence and well-being of older adults, including people living with dementia (PLWD). However, conversational agents have yet to demonstrate a direct impact in supporting target populations at home, particularly with long-term user benefits and clinical utility. We introduce an infrastructure fusing in-home activity data captured by Internet of Things (IoT) technologies with voice interactions using conversational technology (Amazon Alexa). We collect 3103 person-days of voice and environmental data across 14 households with PLWD to identify behavioral patterns. Interactions include an automated well-being questionnaire and ten topics of interest, identified using topic modeling. Although a significant decrease in conversational technology usage was observed after the novelty phase across the cohort, steady state data acquisition for modeling was sustained. We analyze household activity sequences preceding or following Alexa interactions through pairwise similarity and clustering methods. Our analysis demonstrates the capability to identify behavioral patterns, changes in those patterns and the corresponding time periods. We further report that households with PLWD continued using Alexa following clinical events (e.g., hospitalizations), which offers a compelling opportunity for proactive health and well-being data gathering related to medical changes. Results demonstrate the promise of conversational AI in digital health monitoring for aging and dementia support and offer a basis for tracking health and deterioration as indicated by household activity, which can inform healthcare professionals and relevant stakeholders for timely interventions. Future work will use the bespoke behavioral patterns extracted to create more personalized AI conversations.