ArXiv Preprint
There is a global aging population requiring the need for the right tools
that can enable older adults' greater independence and the ability to age at
home, as well as assist healthcare workers. It is feasible to achieve this
objective by building predictive models that assist healthcare workers in
monitoring and analyzing older adults' behavioral, functional, and
psychological data. To develop such models, a large amount of multimodal sensor
data is typically required. In this paper, we propose MAISON, a scalable
cloud-based platform of commercially available smart devices capable of
collecting desired multimodal sensor data from older adults and patients living
in their own homes. The MAISON platform is novel due to its ability to collect
a greater variety of data modalities than the existing platforms, as well as
its new features that result in seamless data collection and ease of use for
older adults who may not be digitally literate. We demonstrated the feasibility
of the MAISON platform with two older adults discharged home from a large
rehabilitation center. The results indicate that the MAISON platform was able
to collect and store sensor data in a cloud without functional glitches or
performance degradation. This paper will also discuss the challenges faced
during the development of the platform and data collection in the homes of
older adults. MAISON is a novel platform designed to collect multimodal data
and facilitate the development of predictive models for detecting key health
indicators, including social isolation, depression, and functional decline, and
is feasible to use with older adults in the community.
Ali Abedi, Faranak Dayyani, Charlene Chu, Shehroz S. Khan
2022-11-07
