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Introduction
Behavioral symptoms of Alzheimer's disease (e.g. delusions, wandering, aggression,
sleep disturbance) lead to increased emergency room visits, caregiver burden, and
transfers to memory care facilities. Sensor technologies may hold the potential to
facilitate early detection and pre-emptive intervention for these symptoms by enabling
continuous passive monitoring in a way that in-person monitoring may not be able to.
We present preliminary data for such an approach using a device called the Emerald,
developed at MIT, which emits low-powered radio signals and can identify and track
parameters related to human behavior (sleep, motion, spatial motion, and respiratory
rate) based on how these waves reflect off the human body. Artificial Intelligence
(AI) algorithms elicit behavioral markers from sensor data. The device does not require
any contact or direct interaction by the person being monitored, thus representing
true passive sensing.
Methods
The Emerald device was installed in the rooms of two dementia patients (N=2) with
behavioral symptoms residing in an assisted living facility (ALF). Motion data was
gathered continuously for a period of three months and was mapped on to spatial location
and time frame. Data processing and analysis occurred simultaneously during the collection
period. Additionally, study staff administered weekly standardized assessments to
both the participant (MMSE) and ALF staff (NPI-NH, CMAI, PAS) to augment data collected
from the Emerald. Device data was compiled and made available to the study clinician
for clinical analysis and identification of emergent behavioral complications.
Results
In both participants, device data were used to identify specific behavioral patterns.
The device detected variations in behavior by time of day, escalations in pacing,
and moments of restlessness throughout the night for both participants. For one participant,
clinical interpretation of device data led to the proposition that the participant
was experiencing Periodic Limb Movement Disorder, which was unbeknownst to the participant
or clinician prior to study participation. The device was able to identify periodic
spasms, which occurred when the person was asleep, and localize these to the patient's
legs. The second participant showed increase pacing, wandering, and motor agitation
before being hospitalized for heightened anxiety and aggression. Device data indicates
the period prior to hospitalization featured increased movement episodes relative
to this participant's baseline.
Conclusions
We propose that behavioral phenotyping using an AI-backed passive sensing approach
is feasible and safe, and that this approach can help digitally phenotype behavior
symptoms in dementia. While the device merits validation against the current standard
of behavior measurement in dementia, its advantages include low cost and ongoing engagement,
and continuous monitoring while giving patients the option of stopping monitoring
at their discretion. Further studies evaluating sensitivity and reliability are warranted
to validate the clinical utility of this device.
This research was funded by
This project is supported by an Innovations grant from the Massachusetts Institute
of Technology.
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Copyright
© 2019 Published by Elsevier Inc.
