Advancements in conversational 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 behavioural patterns. Interactions
include an automated well-being questionnaire and 10 topics of
interest, identified using topic modelling. Although a significant
decrease in conversational technology usage was observed after
the novelty phase across the cohort, steady state data acquisition
for modelling was sustained. We analyse household activity
sequences preceding or following Alexa interactions through
pairwise similarity and clustering methods. Our analysis demonstrates the capability to identify individual behavioural 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., hospitalisations), 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 ageing 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
behavioural patterns extracted to create more personalised AI
conversations.