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found only a small number of studies have examined normal
daily activities in community dwelling adults. Sample size,
study duration, and the type of technologies utilized varied
widely. The evaluation of technologies in controlled settings,
such as research testing sites or hospitals, limits the generalizability
of these systems to real-world applications. However,
sensor-based assessment in controlled settings provides the
ability to examine more complex activities [9], which could
translate into advances for in-home monitoring of instrumental
ADLs.
Outcome measures collected using a home-based assessment
platform from different health and functional domains
are able to detect differences between participants with MCI
and normal cognition [5]. Prior studies demonstrate correlations
between decline or disruption in certain health domains
such as walking speed [11] and sleep [12] with cognitive impairment.
Passive infrared (PIR) sensors used in multiple
home systems are able to detect measures related to activity,
walking speed, and nighttime activity in the bedroom. These
sensors are placed in rooms throughout the home and detect
the presence of an individual within the line of sight of the sensor
and can also be deployed in a line on the ceiling to detect
walking speed [13]. Individuals with lower scores on cognitive
testing show a decline in indoor movement patterns [14] and
a decrease in mean walking speed and total number of daily
walks [7]. Differences in nighttime behavior captured by PIR
sensors demonstrate less movement in bed, less time awake
after sleep onset and fewer number of times up out of bed at
night [15], and a tendency toward shorter sleep times [16] in individuals
with cognitive impairment.
Computer use may also serve as a sensitive measure to
detect changes in cognition. Individuals followed longitudinally
who developed MCI show a significant decrease in the
number of days with computer use, average daily use, and
increased variability in day-to-day use [17]. MCI was also
associated with making significantly fewer total mouse movements,
mouse movements that were more variable and less
efficient, and longer pauses between mouse movements [18].
These findings demonstrate how changes in the performance
of daily activities can serve as markers of early and subtle declines
in cognition.
Continuous assessment provides high-frequency data collection
on multiple different activities and a measure of their
day-to-day variability. There may be a period of increased variability
in an individual's performance on certain tasks before
there is notable cognitive decline. Using an electronic pillbox
to monitor daily medication adherence, increased variability
in the timing of taking medications was associated with
lower cognitive function, while overall adherence was not significantly
correlated to performance on cognitive testing [19].
Significant differences in variability in walking speed distributions
[20] and the coefficient of variance of walking speed
[21]-[23] are seen in individuals with MCI, detected using a
ceiling mounted PIR sensor line. Finally, individuals with
MCI have greater differences in the variability of daily computer
use [17] compared to individuals with normal cognition.
September 2021
Monitoring for changes in variability of performance on a
task may be a more sensitive method for the early detection
of cognitive decline, possibly identifying changes before individuals
or their family and friends begin to notice impairment
in ADLs.
Algorithms that combine data from multiple different sensor
types (e.g., motion and contact sensors, light, temperature)
in home-based platforms can identify differences in activity
patterns and behaviors related to daily activities. Differences
in behavioral patterns are detectable in individuals with dementia,
compared to cognitively normal participants, using
a circadian activity rhythm classifier [24]. Machine learning
classification of daily activity patterns identifies differences
in activity performance in individuals with MCI [25], [26].
Change point detection algorithms are able to detect multiple
differences in performance of daily activities and walking
speed between individuals diagnosed with MCI or dementia
versus cognitively intact individuals [27]. Detecting changes
in multiple physical and activity related outcome measures
from a variety of sensors could form a behavioral signature
that is more sensitive to cognitive decline in individuals.
Some sensor platforms have developed algorithms to detect
changes in daily activities that are then sent to individuals
living at home [28] or to staff at an assisted living facility [29]
through a notification system. Participants using the HomeAssist
platform receive notifications based on sensor-derived
data about ADLs, user and home safety, and social participation.
Users of the platform report perceived improvements in
autonomy, self-regulation, and empowerment [28]. Residents
in an assisted living facility monitored with a sensor platform
that sends health alerts to nurses in the facility when changes
in sensor data patterns are detected show lower levels of decline
in function with less significant changes in gait measures
and walking speed compared to residents receiving usual
care [29]. These studies highlight the potential for sensor platforms
to alert individuals and their care providers to relevant
changes in daily activities to assist with maintaining independence
in the home or supporting care in institutional settings.
Collaborative Aging Research Using
Technology (CART) Sensor Platform
The CART initiative has developed the administrative and
operational infrastructure for a scalable technology platform
to support aging in place research [30]. Through an iterative
design process, a participant, sensor, and data management
system has been created to support monitoring of sensor status,
secure data collection and storage, and the ability to share
data with other research sites and collaborators. This has allowed
for multiple sites to use a system with a similar set of
sensors and backend data collection system. The architecture
of the sensor and data management system was created with
extensibility in mind, so that novel sensors can be incorporated
based on the needs of the research study and work within the
platform. In the following sections, the CART platform will be
described briefly (https://www.ohsu.edu/collaborative-aging-research-using-technology/cart-home),
and issues related
IEEE Instrumentation & Measurement Magazine
69
https://www.ohsu.edu/collaborative-aging-research-using-technology/cart-home https://www.ohsu.edu/collaborative-aging-research-using-technology/cart-home

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