In vivo wireless body communications: State-of-the-art and future directions

Abstract

The emerging in vivo communication and networking system is a prospective component in advancing health care delivery and empowering the development of new applications and services. In vivo communications construct wirelessly networked cyber-physical systems of embedded devices to allow rapid, correct and cost-effective responses under various conditions. This paper surveys the existing research which investigates the state of art of the in vivo communication. It also focuses on characterizing and modeling the in vivo wireless channel and contrasting this channel with the other familiar channels. MIMO in vivo is as well regarded in this overview since it significantly enhances the performance gain and data rates. Furthermore, this paper addresses current challenges and future research areas for in vivo communications.

Full text

In Vivo Wireless Body Communications:
State-of-the-Art and Future Directions
Raed M. Shubair 1,2and Hadeel Elayan 1
1Electrical and Computer Engineering Department, Khalifa University, UAE
2Research Laboratory of Electronics, Massachusetts Institute of Technology, USA
Email: rshubair@mit.edu, Hadeel.W.Elayan@ieee.org,
Abstract—The emerging in vivo communication and network-
ing system is a prospective component in advancing health care
delivery and empowering the development of new applications
and services. In vivo communications construct wirelessly net-
worked cyber-physical systems of embedded devices to allow
rapid, correct and cost-effective responses under various condi-
tions. This paper surveys the existing research which investigates
the state of art of the in vivo communication. It also focuses
on characterizing and modeling the in vivo wireless channel and
contrasting this channel with the other familiar channels. MIMO
in vivo is as well regarded in this overview since it significantly
enhances the performance gain and data rates. Furthermore, this
paper addresses current challenges and future research areas for
in vivo communications.
Keywords- In vivo communication, MIMO in vivo, WBAN.
I. INTRODUCTION
Wireless Body Area Networks (WBANs) is an exciting
technology that promises to bring healthcare monitoring ap-
plications to a new level of personalization. The aim of these
applications is to ensure continuous monitoring of the patients’
vital parameters, while giving them the freedom of moving
thereby resulting in an enhanced quality of healthcare [1]. In
fact, a WBAN is a network of wearable computing devices
operating on, in, or around the body. It consists of a group
of tiny nodes that are equipped with biomedical sensors,
motion detectors, and wireless communication devices [2].
Actually, advanced health care delivery relies on both body
surface and internal sensors since they reduce the invasiveness
of a number of medical procedures [3]. Sensors such as
those shown in Fig. 1 transmit data to monitoring devices of
the hospital Information Technology (IT) infrastructure. Elec-
trocardiogram (ECG), electroencephalography (EEG), body
temperature, pulse oximetry (SpO2), and blood pressure are
evolving as long-term monitoring sensors for emergency and
risk patients [4].
Advanced sensors for chemical, physical and visual applica-
tions will become part of future monitoring platforms to check,
for example, insulin or hemoglobin. The benefit provided by
WBAN is obvious to the patients’ comfort especially for long-
term monitoring as well as complex monitoring during surgery
and medical examinations [4].
One attractive feature of the emerging Internet of Things
is to consider in vivo networking for WBANs as an impor-
tant application platform that facilitates continuous wirelessly
enabled healthcare [5]. In vivo communication, also known
Fig. 1. Sensor network of biomedical monitoring applications.
as Intra Body Communication (IBC), uses the human body
to transmit electrical signals, where the radiated energy is
mostly confined within the body [6]. Internal health monitoring
[7], internal drug administration [8], and minimally invasive
surgery [9] are examples of the pool of applications that
require communication from in vivo sensors/actuators to body
worn/surface nodes. However, the study of in vivo wireless
transmission, from inside the body to external transceivers is
still at its early stages.
Fig. 2. Simplified overview of the in vivo communication network.
Fig. 2 shows the modified network organization for in-
terconnecting the biomedical sensors. The data is not trans-
ferred directly from the biomedical sensors to the hospital
infrastructure as in Fig. 1. Indeed, the sensors send their data
via a suitable low-power and low-rate in vivo communication
link to the central link sensor (located on the body like all
other sensors). Any of the sensors may act as a relay sensor
between the desired sensor and the central link sensor if a
direct connection is limited. An external wireless link enables
the data exchange between the central link sensor and the
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external hospital infrastructure [4].
This paper surveys the existing research which investigates
the state of art of the in vivo communication. It also focuses
on characterizing and modeling the in vivo wireless channel
and contrasting this channel with the other familiar channels.
MIMO in vivo is also regarded in this overview since it
significantly enhances the performance gain and data rates.
Furthermore, this paper proposes some future research areas.
The rest of the paper is organized as follows. In Section II, we
present the state of art of in vivo communication. Conducted
research on in vivo channel characterization is provided in
Section III. The MIMO in vivo system is described in Section
IV. In Section V, future research areas are presented. Finally,
we draw our conclusions and summarize the paper in Section
VI.
II. STATE OF ART OF In Vivo COMMUNICATION
In vivo communication is a genuine signal transmission field
which utilizes the human body as a transmission medium for
electrical signals [10]. The body becomes a vital component
of the transmission system. Electrical current induction into
the human tissue is enabled through sophisticated transceivers
while smart data transmission is provided by advanced encod-
ing and compression. Fig. 3 below shows the main components
of an in vivo communication link.
Fig. 3. In vivo communication for data transmission between sensors enabled
by transmitter and receiver units.
A transmitter unit permits sensor data to be compressed
and encoded. It then conveys the data by a current-controlled
coupler unit. The human body acts as the transmission channel.
Electrical signals are coupled into the human tissue and
distributed over multiple body regions. On the other hand,
the receiver unit is composed of an analog detector unit
that amplifies the induced signal and digital entities for data
demodulation, decoding, and extraction [4].
Developing body transmission systems have shown the
viability of transmitting electrical signals through the human
body. Nonetheless, detailed characteristics of the human body
are lacking so far. Not a lot is known about the impact of
human tissue on electrical signal transmission. Actually, for
advanced transceiver designs, the effects and limits of the
tissue have to be cautiously taken into consideration [4] [11].
III. In Vivo CHANNEL MODELING AND
CHARACTERIZATION
An in vivo channel is depicted as being both inhomogeneous
and very lossy [12]. Basically, in an in vivo channel, the
electromagnetic wave passes through various dissimilar media
that have different electrical properties, as displayed in Fig. 4.
This leads to the reduction in the wave propagation speed in
some organs and the stimulation of significant time dispersion
that differs with each organ and body tissue [13]. The previous
effect coupled with attenuation due absorption by the different
layers result in the degradation of the quality of the transmitted
signal in the in vivo channel.
Fig. 4. In vivo multi-path channel [3].
In addition, since the in vivo antennas are radiating into a
complex lossy medium, the radiating near fields will strongly
couple to the lossy environment. This signifies that the radiated
power relies on both the radial and angular positions; hence,
the near field effect has to be always taken into account when
functioning in an in vivo environment [14]. The electric and
magnetic fields behave differently in the radiating near field
compared to the far field. Therefore, the wireless channel
inside the body necessitates different link equations [15]. It
must be noted as well that both the delay spread and multi-
path scattering of a cellular network are not directly applicable
to near-field channels inside the body. The reason behind this
is the fact that the wavelength of the signal is much longer
than the propagation environment in the near field [12].
The authors in [3] used an accurate human body to investi-
gate the variation in signal loss at different radio frequencies
as a function of position around the body. They noticed
significant variations in the Received Signal Strength (RSS)
which occur with changing positions of the external receive
antenna at a fixed position from the internal antenna [3].
Nevertheless, their research did not take into account the basic
characterization of the in vivo channel. In [16], the authors
used an immersive visualization environment to characterize
RF propagation from medical implants. Based on 3-D elec-
tromagnetic simulations, an empirical path loss (PL) model is
developed in [17] to identify losses in homogeneous human
tissues. Further, numerical and experimental investigations of
biotelemetry radio channels and wave attenuation in human
subjects with ingested wireless implants are presented in [18].
Modeling the in vivo wireless channel including building a
phenomenological path loss model is one of the major research
goals in this field. A profound understanding of the channel
characteristics is required for defining the channel constraints
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and the subsequent systems’ constraints of a transceiver design
[4].
A. Path Loss
Path loss in in vivo channels can be investigated using either
a Hertzian-Dipole antenna or a monopole antenna. The former
case is presented in [12] in which path loss is examined with
minimal antenna effects. The length of the Hertzian-Dipole
is so small resulting in little interaction with its surrounding
environment. The path loss can be calculated as
P ath loss(r, θ, φ)=10∗log10(|E|2
r=0
|E|2
r,θ, φ
)(1)
where rrepresents the distance from the origin, i.e. the
radius in spherical coordinates, θis the polar angle and φ
is the azimuth angle. |E|2
r,θ, φ is the square of the magnitude
of the electric field at the measuring point and |E|2
r=0 is the
square of the magnitude of E field at the origin.
Due to the fact that the in vivo environment is an inhomoge-
neous medium, it is mandatory to measure the path loss in the
spherical coordinate system [12]. The setup of this approach
can be seen in Fig. 5 in which it includes the truncated human
body, the Hertzian-Dipole and the spherical coordinate system.
Fig. 5. Truncated human body with Hertizian-Dipole at the origin in spherical
coordinate system [12].
The latter case is presented in [3]. Actually, monopoles are
good choice of practical antennas since they are small in size,
simple and omnidirectional. The path loss can be measured by
scattering parameters (S parameters) that describe the input-
output relationship between ports (or terminals) in an electrical
system [3]. According to Fig. 6, if we set Port 1 on transmit
antenna and Port 2 on receive antenna, then S21 represents the
power gain of Port 1 to Port 2, that is
|S21|2=Pr
Pt
(2)
where Pris the received power and Ptis the transmitted
power. Therefore, we calculate the path loss by the formula
below
P ath loss(dB)=−20 log10 |S21|(3)
Based on the simulations presented in [12], it can be ob-
served that there is a substantial difference in the behaviors of
Fig. 6. Simulation setup by using monopoles to measure the path loss [12].
the path loss between the in vivo and free space environment.
In fact, significant attenuation occurs inside the body resulting
in an in vivo path loss that can be up to 45 dB greater than the
free space path loss. Fluctuations in the out-of-body region is
experienced by the in vivo path loss. On the other hand, the
free space path loss increases smoothly. The inhomogeneous
medium results as well in angular dependent path loss [12].
B. Comparison of Ex Vivo and In Vivo Channels
The different characteristics between ex vivo and in vivo
channels are summarized in [13] as shown in Table 1 below.
IV. MIMO In Vivo
Due to the lossy nature of the in vivo medium, attaining
high data rates with reliable performance is considered a
challenge [5]. The reason behind this is that the in vivo
antenna performance may be affected by near-field coupling
as mentioned earlier and the signals level will be limited by a
specified Specific Absorption Rate (SAR) levels. The SAR is
a measurement of how much power is absorbed per unit mass
of conductive material, in our case, the human organs [19].
This measurement is limited by the Federal Communications
Commission (FCC) which in turns limits the transmission
power [19].
The authors in [13] analyzed the Bit Error Rate for a MIMO
in vivo system. Actually, by comparing their results to a 2×2
SISO in vivo, it was evident that significant performance gains
can be achieved when using a 2×2MIMO in vivo. This setup
allows maximum SAR levels to be met which results in the
possibility of achieving target data rates up to 100 Mbps if the
distance between the transmit (Tx) and receive (Rx) antennas
is within 9.5 cm [19].
Further, in [5], it was proved that not only MIMO in vivo
can achieve better performance in comparison to SISO systems
but also considerably better system capacity can be observed
when Rx antennas are placed at the side of the body. Fig. 7
compares the in vivo system capacity for front, right side, left
side, and back of the body. In addition, it was noticed that in
order to meet high data rate requirements of up to 100 Mbps
with a distance between the Tx and Rx antennas greater than
12 cm for a 20 MHz channel, relay or other similar cooperative
networked communications are necessary to be introduced into
the WBAN network [5].
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Fig. 7. 2×2MIMO and SISO in vivo system capacity comparison [5].
TABLE I
COMPARISON OF Ex vivo AND In vivo CHANNEL [13]
Features Ex vivo In vivo
Physical Wave
Propagation
Constant speed
Multipath- reflection, scattering
and diffraction
Variable speed
Multipath and penetration
Attenuation and
Path Loss
Lossless medium
Decreases inversely with dis-
tance
Very lossy medium
Angular (directional) depen-
dent
Dispersion Multipath delays-time disper-
sion
Multipath delays of
variable speed -frequency
dependency-time dispersion
Directionality Propagation essentially uniform Propagation varies with di-
rection
Directionality of antennas
changes with position
Near Field
Communications
Deterministic near-field region
around the antenna
Inhomogeneous medium -
near field region changes
with angles and position in-
side the body
Power
Limitations
Average and Peak Plus specific absorption rate
(SAR)
Shadowing Follows a log normal distribu-
tion
To be determined
Multipath Fading Flat fading and frequency selec-
tive fading
To be determined
Antenna Gains Constant Angular and positional de-
pendent
Gains highly attenuated
Wavelength The speed of light in free space
divided by frequency λ=c
√rf(4)
at 2.4GHz, average dielec-
tric constant r=35,
which is roughly 6 times
smaller than the wavelength
in free space.
V. F UTURE DIRECTIONS
The major future research direction focuses on investigating
high throughput, efficient, and robust novel networking tech-
nologies that enable reliable information transmission between
devices. In order to achieve a system with such specifications,
great focus should be spent on architecting, realizing and
networking a family of wirelessly controlled in vivo devices.
In addition, electronic and mechanical miniaturization of com-
plex systems are necessitated. Basically, motion control, video
zoom, auto focus and LED illumination are some of the
various functions that require careful control of the in vivo
wireless devices.
The first aspect that should be regarded in order to ensure
improved future in vivo communication is frequency. Indeed,
the frequency range selected for such communications plays a
significant role in the design and performance of the system.
Actually, there is a direct relationship between frequency and
tissue warming in which the higher the frequency of the
electromagnetic signal, the higher is its absorption by the
tissue, thereby the greater the tissue warming. Thus, it is
favorable to use lower frequencies for communications. Yet,
the lower the frequency, the larger the antenna dimensions.
As a result, a tradeoff between antenna dimensions and greater
tissue warming should be achieved [20]. Moreover, limitations
exist when transmitting at high frequencies from in vivo
devices to ex vivo transceivers since the maximum transmit
power is restricted by the SAR safety guidelines [19]. Even
when operating under low noise conditions with moderate
BER requirements, reliable data transmission to an external
receiver can only be achieved when located very close to
the body. Therefore, when noise levels increase or the BER
becomes more stringent, a relay network or use of multiple
antennas is essential to achieve high data rates [19]. Based on
the simulations conducted by [19] , the maximum SAR levels
occur at points closest to the transmit antenna. Consequently,
it could be concluded that by placing the transmitter further
from organs, the power levels could be possibly increased to
obtain higher signal levels at the external receiver.
In addition, the minimum transmission power levels re-
quired to establish a reliable wireless data connection must
be recorded with the implants placed in different locations
inside the body. The first aim of this data is to compare the
power levels with international safety regulations, limiting the
human tissues exposure to radio frequency signals. The second
objective is to give reference levels for power consumption
during data transmission that can be useful to estimate battery
lifetime, since this task is one of the most energy demanding
for the implant, particularly, if compared with sensor data
acquisition.
Furthermore, a substantial feature in future in vivo research
is the development of parametric models for the in vivo
channel response which can have a beneficial effect on the
optimization of advanced communication techniques. This in-
volves the statistical characterization of the in vivo channel and
the usage of MIMO technology for improved communication
reliability and performance. Channel models are required to
achieve accurate link budget which aids in finding optimized
locations for placing the transmitter and receiver on and inside
the human body [21]. On the other hand, MIMO in vivo
capacity should be theoretically studied incorporating both the
near-and far field effects [13].
It must be noted as well that the channel at the head in
in vivo communication is of a particular interest since most
human communication organs such as mouth, ears and eyes
are located there. Therefore, it is suggested to consider the ear
to ear link in simulations, for it is the worst case scenario at
the head as it lacks the line of sight component [22].
As a result of the diversity found in the human body
including fluids, fats, bones, and muscles, variation in energy
absorption exists coupled with the possibility of tissue damage
due to heating by radiation. Such issues result in complicating
the design of biosensors and biosensor networks; therefore,
careful attention must be paid in future work to overcome
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such limitations [20]. Another particular concern is biocom-
patibility. Basically, any sensor must not only be biologically
inert, but also it should not perturb the normal biology its
interrogating [23].
Nanoscale in vivo devices are another major aspect of
biologically inspired network systems since they open up
amazing opportunities in health care. Indeed, these systems
can be introduced into the human body and could intervene
very closely with organs and cells. Nanodevices intervening at
the molecular level will allow novel healthcare solutions to be
developed which will be not only more efficient but also cost-
effective because of the possibility of large-scale production
[24].
Finally, testing using real animals to validate the simulation
results is one of the fundamental research directions [21] in
addition to hardware emulation with human body phantoms.
VI. CONCLUSION
In this paper, an overview of the in vivo communication and
networking is provided. The overview focuses on the state of
art of the in vivo communication, the in vivo channel modeling
and characterization, and the concept of MIMO in vivo.The
paper also discusses few potential research areas covering the
development of parametric models and the in-depth study of
MIMO in vivo technology. Frequency range, power levels and
SAR requirements are few aspects that should be carefully
regarded in any upcoming research associated with this field.
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