16-01-2013, 03:52 PM
Wireless Sensor Networks for Healthcare
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ABSTRACT
Driven by the confluence between the need to
collect data about people’s physical, physiological, psychological,
cognitive, and behavioral processes in spaces ranging from
personal to urban and the recent availability of the technologies
that enable this data collection, wireless sensor networks for
healthcare have emerged in the recent years. In this review, we
present some representative applications in the healthcare
domain and describe the challenges they introduce to wireless
sensor networks due to the required level of trustworthiness
and the need to ensure the privacy and security ofmedical data.
These challenges are exacerbated by the resource scarcity that
is inherent with wireless sensor network platforms. We outline
prototype systems spanning application domains from physiological
and activitymonitoring to large-scale physiological and
behavioral studies and emphasize ongoing research challenges.
INTRODUCTION
Driven by technology advances in low-power networked
systems and medical sensors, we have witnessed in recent
years the emergence of wireless sensor networks (WSNs)
in healthcare. These WSNs carry the promise of drastically
improving and expanding the quality of care across a wide
variety of settings and for different segments of the population.
For example, early system prototypes have demonstrated
the potential of WSNs to enable early detection of
clinical deterioration through real-time patient monitoring
in hospitals [13], [43], enhance first responders’ capability
to provide emergency care in large disasters through
automatic electronic triage [24], [50], improve the life
quality of the elderly through smart environments [72],
and enable large-scale field studies of human behavior and
chronic diseases [45], [58].
BACKGROUND
Medical Sensing
There is a long history of using sensors in medicine and
public health [2], [74]. Embedded in a variety of medical
instruments for use at hospitals, clinics, and homes, sensors
provide patients and their healthcare providers insight into
physiological and physical health states that are critical to the
detection, diagnosis, treatment, andmanagement of ailments.
Much of modern medicine would simply not be possible nor
be cost effective without sensors such as thermometers, blood
pressure monitors, glucose monitors, electrocardiography
(EKG), photoplethysmogram (PPG), electroencephalography
(EEG), and various forms of imaging sensors. The ability to
measure physiological state is also essential for interventional
devices such as pacemakers and insulin pumps.
Medical sensors combine transducers for detecting electrical,
thermal, optical, chemical, genetic, and other signals
with physiological origin with signal processing algorithms to
estimate features indicative of a person’s health status.
Sensors beyond those that directly measure health state have
also found use in the practice of medicine. For example,
location and proximity sensing technologies [39] are being
used for improving the delivery of patient care and workflow
efficiency in hospitals [22], tracking the spread of diseases by
public health agencies [28], and monitoring people’s healthrelated
behaviors (e.g., activity levels) and exposure to
negative environmental factors, such as pollution [58].
HEALTHCARE APPLICATIONS
Wirelessly networked sensors enable dense spatio–
temporal sampling of physical, physiological, psychological,
cognitive, and behavioral processes in spaces ranging
from personal to buildings to even larger scale ones. Such
dense sampling across spaces of different scales is resulting
in sensory information based healthcare applications
which, unlike those described in Section II-A, fuse and
aggregate information collected from multiple distributed
sensors. Moreover, the sophistication of sensing has
increased tremendously with the advances in cheap and
miniature, but high-quality sensors for home and personal
use, the development of sophisticated machine learning
algorithms that enable complex conditions such as stress,
depression, and addiction to be inferred from sensory
information, and finally the emergence of pervasive
Internet connectivity facilitating timely dissemination of
sensor information to caregivers.
Trustworthiness
Healthcare applications impose strict requirements on
end-to-end system reliability and data delivery. For
example, pulse oximetry applications, which measure the
levels of oxygen in a person’s blood, must deliver at least
one measurement every 30 s [37]. Furthermore, end users
require measurements that are accurate enough to be used
in medical research. Using the same pulse oximetry
example, measurements must deviate at most 4% from
the actual oxygen concentrations in the blood [37]. Finally,
applications that combine measurements with actuation,
such as control of infusion pumps and patient controlled
analgesia (PCA) devices, impose constraints on the end-toend
delivery latency. We term the combination of data
delivery and quality properties the trustworthiness of the
system and claim that medical sensing applications require
high levels of trustworthiness.
Privacy and Security
Wireless sensor networks in healthcare are used to
determine the activities of daily living (ADL) and provide
data for longitudinal studies. It is then easy to see that such
WSNs also pose opportunities to violate privacy. Furthermore,
the importance of securing such systems will
continue to rise as their adoption rate increases.
The first privacy challenge encountered is the vague
specification of privacy. The Heath Insurance Portability
and Accountability Act (HIPPA) by the U.S. Government is
one attempt to define this term [1]. One issue is that
HIPPA as well as other laws define privacy using human
language (e.g., English), thus, creating a semantic nightmare.
Nevertheless, privacy specification languages have
been developed to specify privacy policies for a system in a
formal way. Once the privacy specifications are specified,
healthcare systems must enforce this privacy and also be
able to express users’ requests for data access and the
system’s policies. These requests should be evaluated
against the predefined policies in order to decide if they
should be granted or denied. This framework gives rise to
many new research challenges, some unique to WSNs, as
we describe in the paragraphs that follow.
SYSTEMS
Next, we present several wireless sensing system prototypes
developed and deployed to evaluate the efficacy of
WSNs in some of the healthcare applications described in
Section III. While wireless healthcare systems using
various wireless technologies exist, this work focuses on
systems based on low-power wireless platforms for physiological
and motion monitoring studies, and smartphonebased
large-scale studies.
Physiological Monitoring
In physiological monitoring applications, low-power
sensors measure and report a person’s vital signs (e.g.,
pulse oximetry, respiration rate, temperature). These applications
can be developed and deployed in different contexts
ranging from disaster response, to in-hospital patient monitoring,
and long-term remote monitoring for the elderly.
While triage protocols for disaster response already exist
(e.g., [31] and [70]), multiple studies have found that they
can be ineffectual in terms of accuracy and the time to
transport as the number of victims increases in multicasualty
incidents [5], [65]. Furthermore, studies in hospitals report
that patients are left undermonitored [15] and emergency
departments today operate at or over capacity [4]. Finally,
anecdotal evidence suggest that this lack of patient
monitoring can lead to fatalities [14], [54], [68].
Motion and Activity Monitoring
Another application domain for WSNs in healthcare is
high-resolution monitoring of movement and activity
levels. Wearable sensors can measure limb movements,
posture, and muscular activity, and can be applied to a
range of clinical settings including gait analysis [60], [64],
[73], activity classification [29], [52], athletic performance
[3], [51], and neuromotor disease rehabilitation [49], [57].
In a typical scenario, a patient wears up to eight sensors
(one on each limb segment) equipped with MEMS accelerometers
and gyroscopes. A base station, such as a
PC-class device in the patient’s home, collects data from
the network. Data analysis can be performed to recover the
patient’s motor coordination and activity level, which is in
turn used to measure the effect of treatments.
FUTURE DIRECTIONS
Driven by user demand and fueled by recent advances in
hardware and software, the first generation of wireless
sensor networks for healthcare has shown their potential to
alter the practice of medicine. Looking into the future, the
tussle between trustworthiness and privacy and the ability
to deploy large-scale systems that meet the applications’
requirements even when deployed and operated in
unsupervised environments is going to determine the
extent that wireless sensor networks will be successfully
integrated in healthcare practice and research