Instrumentation & Measurement Magazine 24-2 - 10

are prone to neglect. Furthermore, monitoring systems that
use video/camera surveillance methods are unwelcomed by
many people because of privacy invasion.
On the other hand, non-invasive instrumentation and measurement methods use devices embedded in the environment
or the body of the elderly person. The main advantage of noninvasive methods is that they collect information continuously
and require minimum intervention from the patient. Although
the development of non-invasive measurement methods is
still in its infancy stage, the advancement of micro and nanotechnologies indicates a promising future for these methods to
meet the quality and accuracy desired. As shown in [8], there
are encouraging research and development efforts to use less
invasive technologies to monitor the health of people. For example, contact lens sensors are used to analyze blood sugar
composition in tears without using a needle, saliva biosensors
and ingestible devices are used for glucose diagnoses and delivery of drugs. These human sensing sites offer great potential
to install sensing devices to monitor the health of the elderly
and provide treatment of many diseases and conditions. Heart
monitoring tools have seen significant development lately, as
well. Older people do not need to be hooked to machines or
carry straps to monitor their heart rate. Instead, they can use
a nonintrusive wrist band equipped with giant magnetoresistance sensors or simply smartwatches to monitor their heart
rate, which is reported directly to the caregivers. Another
example of less invasive technology for ADL is the use of ultrawideband (UWB) technology to monitor the elder's activities
and behavior. UWB is used for localization and see-throughwall radar systems for fall detection without the need for video
surveillance.

coupled with edge computing services (e.g., fog nodes) closer
to the source of the measurement. Another advantage of an
edge computing service includes storage and caching of information for rapid delivery to interested healthcare providers
in the monitoring layer. The work in [10] focuses on creating
Over-The-Air (OTA) reconfiguration of wearable sensors and
devices for monitoring systems using a central processing and
communication hub (CPH). The idea of the CPH is to provide
on-demand network parameter reconfiguration to overcome
the issues of interoperability among heterogeneous wearable
devices. CPH eliminates conversion interfaces among sensor devices and allows for real-time delivery of measurement
packets. Parallel to these efforts, there are noted advancements in the development of the Internet of Bio-nano Things
(IoBNT) that can seemingly integrate into the existing ICT infrastructure. In [11], the authors propose an IoBNT framework
that connects nanoscale biosensors to take measurements directly from human cells for increased accuracy and to process
them in real-time via electromagnetic waves and molecular
communication.

Challenges and Open Research Issues
This article described how current monitoring systems need to
consider new instrumentation and measurement methods to
detect and control the spread of deadly viruses and keep track
of the well-being of older people. I discussed recent research
that is making significant progress in the field. However,
there are still several open research challenges on monitoring system measurements that became apparent during the
COVID-19 crisis. Next, I provide a general view of these challenges and the road map for further research.

Enabling ICT Methods of Real-Time
Measurement for Elderly Monitoring Systems

Reliability Measurement of Physiological
Parameters

Remote monitoring systems use ICT technologies to collect
and transform digital measurement data into essential health
services for older people. During the COVID-19 pandemic,
real-time measurement is critical to detect time-sensitive
healthcare crises. Monitoring systems consist of a wide range
of devices where each is tasked to collect health-related measurement data. These devices are connected using various
network structures and protocols in which transmission latency is accumulated as information propagates through the
system. For example, a wireless body area network (WBAN)
uses a local node to collect measurements from sensors attached to the body and then passes the data to the local area
network (LAN) gateway, which intern transmits the information to a processing center. The time difference between
successive measurements is subject to the added latency at
each transmission stage. Currently, researchers are exploring the emerging IoT platform to streamline the monitoring
system as one processes from the sensor level to the analysis
level. For example, the work in [9], suggests a layered approach where data measurements are collected from an IoT
layer and then processed at the network edge using fog nodes.
The main idea is that latency would be reduced when IoT is

Wearable and biosensor devices that track various physiological metrics are emerging all over the healthcare and wireless
industries. Indeed, there are numerous wearable devices that
are popular, but not all of them can be reliable to indicate something about our health status genuinely. It is a crowded market,
where physiological metrics become commodities of attractive features. For the average person, it is perplexing to decide
which wearable or biosensing device is better for them. Measuring and estimating the reliability of data and devices in a
statistically satisfactory method is more important now than
ever before [12]. The challenge is to design standard models
to measure the accuracy and usefulness of health parameters
from these devices and evaluate them using clinical data and
benchmarking tools. There is also a need for research on clinical protocols that model, measure, and estimate the reliability
of physiological parameters from these devices. Furthermore,
the COVID-19 crisis revealed that there is also a need for AIbased computational platforms to assess physiological and
behavioral patterns of the elderly and to make a reliable prediction to whether there is a match between physiological metric
parameters and those of COVID-19. In this sense, the reliability of the physiological parameters and their measurement is

10	

IEEE Instrumentation & Measurement Magazine	

April 2021



Instrumentation & Measurement Magazine 24-2

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