Aerospace and Electronic Systems Magazine May 2018 - 9

Cisotto and Pupolin
pacemakers moved from the use of alternating current-powered
vacuum tube electronics to current solutions using microelectronics and lithium batteries. Similarly, small automatic pumps for insulin injection are available for patients suffering from diabetes.
While the spread of very sophisticated implantable sensors,
e.g. pacemakers and insulin pumps, is limited by costs, invasiveness, and recipient patients, wearable sensors are seeing a sudden
growth in their market, where both patients and healthy subjects
(especially in the sports) aim to intensively monitor their own
health conditions. This is due to their low-cost, large-scale availability and noninvasiveness that give people the capability to be
aware of their own health or sport performance and to take actions to improve them. Therefore, smartwatches, smartphones, and
wearable straps and bands provided with accelerometers, pulse
oximeters, simple heart rate monitors, as well as GPS and other
simple sensors for track vitals, are becoming more and more popular. It is interesting to note that, while at the beginning low-cost
wearable devices were mostly employed to improve performance
in sports by healthy individuals, more recently, they have been included in the most advanced tele-monitoring platforms addressing
a large and heterogeneous population of patients, gaining attention
and founding by several founding agencies all around the world.
Nevertheless, sensing has recently come with issues related to
interconnection between sensors: they typically communicate via
wireless links characterized by a specific topology and communication protocol, depending on requirements and constraints of the
particular application.
At the same time, sensors are devices able to acquire data as
well as perform computations: mostly, a trade-off between centralized and distributed computation has to be taken into account.
Nowadays, the hardest constraint is given by the batteries duration inside devices: some of them last for hours, others need to survive in a continuous-mode operation for days and months (invasive
devices are required to prolong their life to few years). There are
many factors that could influence the duration of batteries: mode of
operation, local processing, and communication protocols. Indeed,
bursty (infrequent and short-time) or continuous mode of operation
could be easily seen as one of the most important parameters to
evaluate when design such a tele-monitoring system. At the same
time, compression and local aggregation of data could represent
a necessary step to be locally performed at the individual sensor
level, while more demanding signal processing could be assigned
to main hubs or central servers (typically supplied with unlimited
power). Finally, the particular topology of the sensors infrastructure, as well as the energy resources needed to implement the communication protocol within-network and between-networks, could
be further consume battery energy.
Therefore, intense and dynamic research has been ongoing for
scavenging [13] and harvesting energy from alternative resources,
e.g. capturing energy from the ambient, as well as from the human
body itself (through its movement or the gradient of temperature
over its surface).
To this purpose, environmental sensors could be also added to
the network, in order to gather measures such as the ambient temperature, the humidity level, the wind strength (if outdoor), and so
forth. This information could be then complement for the vital sign
MAY - JUNE 2018

measurements in order to provide a comprehensive (quantitative)
picture of the living conditions of patients and athletes.
Overall, the mainstreams of academic as well as industrial
research focus on the development of efficient communication
protocols (5G), unobtrusive hardware design as well as smart data
analytics to prolong the battery life of sensors within the network
(IoT,), thus providing a pervasive and customized monitoring environment to improve the QoL.

ANALYTICS/SIGNAL PROCESSING
Signal processing for HRQoL generally labels a set of software
tools which deal with the transformation of data into different
domains, e.g., frequency, compressed or others, performed at the
sensor level, at a central hub, e.g., in a star-topology network, or
offline to carry detailed and smart analysis. Typically, three possible kinds of data are involved in a system providing HRQoL:
biosignals, event information, and multimedia [9].
Biosignals can include vital signs and other signals such as the
electrocardiogram (ECG or EKG), the electromyogram (EMG),
and the electroencephalogram (EEG). They are usually recorded by
dedicated instrumentation, but low-cost and portable solutions are
currently available to perform some rough, but still useful, measurements. They all include a differential measurement of the potential difference between one location, i.e. point of interest, and a
reference location (typically, selected to be located nearby). ECG,
EMG, and EEG, as well, allow for a multichannel recording, that is,
multiple sensors are placed over the chest, the skin, and the scalp of
the individual, respectively, and signals are acquired in a synchronous way from all available electrodes, simultaneously. This gives
tremendous advantages on the way to investigate abnormal behaviors in details and to extract the most explicative (even complex)
features to synthetize the individual's behavior or health condition.
Event information includes emergency alarms, fall detections,
and other bursty data traffic that could be generated at specific
time instants (mostly, infrequently). They allow caregivers and the
medical staff to promptly provide intervention to cope with any
emergency or to collect such kind of data for further processing.
Multimedia can embrace a large variety of data, mostly multidimensional and continuously recorded over time, such that threedimensional images from imaging (magnetic resonance imaging,
positron emission tomography scans, and radiology), video or
VoIP, but also gait over dedicated sensorized paths as well as virtual reality for augmented experience of reality.
Processing of all such data could be performed into two very
different modes: (i) real-time and (ii) offline.
The first is typically implemented in case of closed-loop applications and alarm delivery systems that require low computationally demanding and (generally) coarse features to be extracted,
within very short time lapses. Fast and low-demanding signal processing may also include compression at the sensors level as well
as data aggregation at the hub or at the central server level: compression and aggregation can contribute to lower the energy demanding of the network (communication protocols) and to prolong
the life of sensors battery and, ultimately, of the network itself.
Indeed, IoT for health and the most advanced 5G communication

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