Instrumentation & Measurement Magazine 24-6 - 16

with dementia repeated
themselves more from dayto-day
than those without
dementia. One might suspect
that users would
object to the idea of their
phone calls being recorded
and analyzed; however, it
appears that even just analyzing
the frequency of
conversation, without recording
the audio itself, is
correlated with some measures
of mental well-being
[22].
Fig. 2. A number of audio sensing options for smart homes have been proposed. Here, arrows show the flow of
information (raw data, results of automatic processing, physician-verified health information) through the system.
regularly-scheduled, audio-recorded interviews for the purposes
of detecting cognitive status through speech.
A potential drawback to these types of active assessments is
that they place a burden on the participant to complete, which
over time may lead to boredom, apathy, or non-compliance.
Depending on the nature of the tasks, they may also suffer
from a learning effect, much like traditional cognitive testing.
They also assume some level of technological fluency for the
user to log on to the device and complete the procedure [19].
Passive Sensing
Passive sensing frameworks have the benefit of collecting
speech data unobtrusively over long periods of time. However,
they also generate more technical challenges, as well as
ethical issues surrounding privacy and consent, which will be
discussed in more detail below. First, we examine some of the
proposed passive sensing technologies.
Smartphone conversation recording: As an increasing proportion
of the population owns a smart phone, it is a natural
choice for recording health data, including variables such
as movement, location, heart rate, respiration, and of course
voice. In [20], conversations were recorded between participants
with early dementia and a family caregiver, and
acoustic and linguistic processing was used to identify the
speakers with dementia with 92% accuracy. While the conversations
were actually recorded live, the author proposed that a
similar methodology could be used to analyze phone conversations
with family members [20]. A group from IBM Research
recorded regular phone conversations between elderly individuals
and a monitoring service who called to check in on
their clients at home [21]. They found that the participants
16
Wearable devices: Wearable
recording devices, as
a category distinct from
smart phones, have been
considered in a few research
studies. A group
at Cornell used a mobile
sensing device to capture
both physical activity and
audio data from older adults in a continuing care retirement
community. Acoustic features were extracted from the speech
signal without actually recording the raw data, to protect privacy.
The proportion of time spent in conversation correlated
with mental health scales for social functioning, as well as correlating
negatively with a depression scale [23]. In another
study, researchers used a wearable electronically-activated recorder
to intermittently sample audio snippets throughout the
day and found that markers of complex, analytic, and specific
language correlated with measures of executive function and
working memory [24].
Smart home virtual assistants: One exciting possibility for
speech sensing in the home is the use of virtual assistants. Virtual
assistants (such as Google Home and Amazon Alexa)
provide a natural interface to smart home features and assistive
technologies, with older adults finding voice interfaces
more user-friendly than touch-based tablet interfaces [25].
Technological advances in natural language processing and
machine learning are making virtual assistants more capable.
This enables a richer conversation between user and the
virtual assistant which in turn could provide a rich source of
information for long-term study of cognitive status in a smart
home environment. Since these spoken interactions would be
analyzed in any case for the purposes of question-answering
and information retrieval, it would require minimal additional
processing to extract features relevant to cognitive status.
With work in this area ongoing, preliminary results have
shown promise: researchers found statistically significant
correlations between MMSE scores and features measuring
pauses, hesitations, and error-handling when older adults
IEEE Instrumentation & Measurement Magazine
September 2021

Instrumentation & Measurement Magazine 24-6

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