A Statistical Path Loss Model for Medical Implant 

Communication Channels

Kamran Sayrafian-Pour, Wen-Bin Yang, John 

Hagedorn, Judith Terrill 

Information Technology Laboratory 

National Institute of Standards and Technology 

Gaithersburg, Maryland, USA 

{ksayrafian, wyang, hagedorn, Judith.terrill}@nist.gov 

Kamya Yekeh Yazdandoost 

Medical ICT Institute 

National Institute of Information and Communications Technology 

New Generation Wireless Communications Research Center 

Yokosuka, Japan 

yazdandoost@nict.go.jp

Abstract — Knowledge of the propagation media is a key step 

toward a successful transceiver design. Such information is 

typically gathered by conducting physical experiments, 

measuring and processing the corresponding data to obtain 

channel characteristics. In case of medical implants, this could be 

extremely difficult, if not impossible. In this paper, an immersive 

visualization environment is presented, which is used as a 

scientific instrument that gives us the ability to observe RF 

propagation from medical implants inside a human body. This 

virtual environment allows for more natural interaction between 

experts with different backgrounds, such as engineering and 

medical sciences. Here, we show how this platform has been used 

to determine a statistical path loss model for medical implant 

communication systems. 

Keywords – Channel model; Implant Communication system; 

Immersive visualization system; Body Area Networks 

I. INTRODUCTION

Body Area Networks which consist of RF-enabled wearable 

and implantable sensory nodes are poised to be a promising 

interdisciplinary technology with novel uses in pervasive 

health information technology. However, numerous challenges 

including size, cost, energy source, sensing/actuator 

technology, transceiver design still need to be resolved [3]. 

Radio-enabled implantable sensor nodes offer a revolutionary 

set of applications, among which we can point to smart pills 

for precision drug delivery, glucose monitors, blood pressure 

sensing system, and eye pressure sensors for glaucoma 

patients. Some recent advances in microelectronics indicate 

that the technology to achieve ultra-small and ultra low power 

devices is completely within reach. 

The Federal Communication Commission (FCC) has 

allocated the frequencies in the 402-405 MHz range to be used 

for Medical Implant Communication Services (MICS). This is 

an ultra-low power, unlicensed mobile radio service for 

transmitting data in support of diagnostic or therapeutic 

functions associated with implanted medical devices. The 

MICS permits individuals and medical practitioners to utilize 

ultra-low power medical implant devices, such as cardiac 

pacemakers and defibrillators without causing interference to 

other users of the electromagnetic radio spectrum. Among 

primary reasons for selecting these frequencies, one can point 

to better propagation characteristics for medical implants, 

reasonable sized antennas and worldwide availability. 

Knowledge of RF propagation for implantable devices will 

assist RF engineers to optimize physical layer design and 

therefore, achieve better performance. Such information is 

typically gathered by conducting physical experiments, 

measuring and processing the corresponding data to obtain 

channel characteristics. In case of medical implants, this could 

be extremely difficult if at all possible. 

In this paper, we use a sophisticated and innovative 3D 

virtual reality simulation platform to study electromagnetic 

propagation from medical implants. Communication with an 

implanted device can be done from any direction inside or 

outside the body due to various body postures and human 

motion. Consequently, a true 3D environment is needed to 

better capture, visualize and understand RF propagation 

from/to implants. In the following sections, we describe such a 

platform and show how it was used to extract a simple 

statistical path loss model for MICS channels. 

The rest of this paper is as follows. Section 2 will describe 

the immersive 3D platform that we have constructed to study 

RF propagation from radio implants. Brief description of our 

approach and the simulated scenarios is provided in Section 3. 

Then, the statistical path loss model is described in Section 4, 

and finally concluding remarks and future plans are expressed 

in Section 5. 

II. A 3D VISUAIZATION SYSTEM FOR MEDICAL

IMPLANTS 

Figure 1 shows the block diagram of our simulation system. 

As observed, the main components of this system include: a 

three-dimensional human body model, the propagation engine 

which is a three-dimensional full-wave electromagnetic field 

simulator (i.e. HFSS 1 ), the 3D immersive & visualization 

platform, and finally an implantable (or body surface antenna). 

 1 HFSS is registered trademark of Ansoft Corporation. The HFSS has been 

used in this research to foster understanding. Such identification does not 

imply recommendation or endorsement by the National Institute of Standard 

and Technology, nor does it imply that this product is necessarily the best 

available for the purpose.
The 3D human body model includes frequency dependent 

dielectric properties of 300+ parts in a male human body. 

These properties are also user-definable if custom changes or 

modifications are desired. The human body model has a 

resolution of 2 mm. The HFSS propagation engine enables us 

to compute a variety of different electromagnetic quantities 

such as the magnitude of electric and magnetic fields and 

Specific Absorption Rate (SAR). 

The 3D immersive platform as shown in Fig. 2 includes 

several important components: three orthogonal screens that 

provide the visual display, the motion tracked stereoscope 

glasses, and the hand-held motion tracked input device. The 

screens are large projection video displays that are placed edgeto-edge in a corner configuration. These three screens are used 

to display a single three-dimensional (3D) stereo scene. The 

scene is updated based on the position of the user as 

determined by the motion tracker. This allows the system to 

present to the user a 3D virtual world within which the user can 

move and interact with the virtual objects. The main interaction 

device is a hand-held three button motion-tracked wand with a 

joystick. 

This virtual environment allows for more natural 

interaction between experts with different backgrounds such as 

engineering and medical sciences. The user can look at data 

representations at any scale and position, move through data, 

change orientation, and control the elements of the virtual 

world using a variety of interaction techniques including 

measurement and analysis [2]. All of these 

capabilities are extremely useful when studying RF 

propagation to/from medical implants. 

Human Body

Model

Propagation Engine 3D Immersive

Platform

Input

Parameters

Human Body

Model

Propagation Engine 3D Immersive

Platform

Input

Parameters

Figure 1. System block diagram 

Figure 2. A User in the NIST Immersive Visualization Environment 

The final component of our system is the implant antenna. The 

operating environment for an implant antenna is quite different 

from the traditional free space communication. Designing an 

efficient antenna for implantable devices is an essential 

requirement for reliable MICS operation. The dimension of the 

antenna must be very small and it should be long term 

biocompatible [5]. The antenna must also be electrically 

insulated from the body to avoid possible short circuits. 

Figure 3 displays the implant antenna used in our 

simulations. The antenna is composed of a single metallic layer 

and is printed on a side of a D51(NTK) substrate with 

dielectric constant of = 30 r ε , loss tangent of 

tanθ = 0.000038 , and thickness of 1 mm. A copper of 0.036 

mm thickness has been used as a metallic layer. The dimension 

of the antenna is 8.2 x 8.1 x 1 mm which is quite appropriate 

for some medical applications. The metallic layer is covered by 

RH-5 substrate with dielectric constant of = 1.0006 r ε , loss 

tangent of tanθ = 0 , and thickness of 1 mm. The simulated 

return loss of this antenna is shown in Fig. 4. Good impedance 

matching in the MICS frequency band (i.e. 402-405 MHz) is 

observed. Further details on the design and performance of this 

antenna can be found in [1,6]. 

Figure 3. The layout of the implant antenna 

Figure 4. Return loss of the implant antenna 

Input parameters to our system include: antenna characteristics, 

antenna position, antenna orientation, operating frequency, 

transmit power, resolution, range and the choice of the desired 

output parameters. The operating frequency in this study has 

been chosen to be 403.5 MHz which is the mid-point of the 

MICS frequency band. Resolution of 4 mm has been selected 

to run the simulation and a range (i.e. distance) of 50 cm from 

the transmitting antenna has been considered. 
Figure 5 displays a sample output image of our system when an 

antenna is located in the right arm. The color map shows the 

received signal strength on the surface of the body. 

Figure 5. Sample output image of the immersive system representing signal 

strenght 

III. APPROACH 

Simulations have been performed for four near-surface 

implants and two deep-tissue implants applications in a typical 

male body. The near-surface scenarios include applications 

such as Implantable Cardioverter-Defibrillator (ICD) and 

Pacemaker (located below the left pectoral muscle), Vagus 

Nerve Stimulation (Right Neck & Shoulder) and two Motion 

Sensor applications located in right hand and right leg. These 

locations are shown by the white circles in Fig. 6. The deep 

tissue implant scenarios considers endoscopy capsule 

applications for upper stomach (95mm below body surface) 

and lower stomach (118mm below body surface) as 

highlighted by the red circles in Fig. 6. 

 

. 

.

Figure 6. Various transmitter locations for obtaining RSS 

For each scenario (i.e. TX location), the received power was 

calculated for a grid of points within a cylinder area around the 

body as shown in Fig. 7. Then, the resulting data was 

partitioned into three sets: in-body to in-body, in-body to body 

surface, and in-body to out-body propagation sets. The in-body 

to in-body set includes all of the sample points that completely 

reside inside the body. Likewise, the in-body to body surface 

set includes all points that reside within a definable distance 

(i.e. 2 mm, 10 mm, and 20 mm) from the body surface; and 

finally the in-body to out-body propagation set distinguishes all 

of the points that reside further away from the body surface. 

Figure 7. Partitioning the simulation data points 

IV. STATISTICAL PATH LOSS MODEL 

In our calculation, path loss at a distance d from the 

transmitting antenna is defined as: 

( ) ( ) P d

G P PL d

R

R T =

where PT is the transmit power, PR denotes the received power 

and GR is the receiver antenna gain. Therefore, as defined in 

above, the path loss would include the transmitter antenna gain. 

This is usually not the case for channel models corresponding 

to most wireless systems, but for MICS, the transmitting 

antenna is considered to be part of the channel [4]. 

The path loss in dB at some distance d can be statistically 

modeled by the following equation: 

0 10 0 0 PL(d) = PL(d ) +10nlog (d / d ) + S d ≥ d

where 0 d is the reference distance (i.e. 50 mm), and n is the 

path loss exponent which heavily depends on the environment 

where RF signal is propagating through. For, example, it is 

well known that for free space, n=2. Human body is an 

extremely lossy environment; therefore, much higher value for 

the path loss exponent is expected. S is the random scatter 

around the mean and represents deviation in dB caused by 

different materials (e.g., bone, muscle, etc.) and antenna gain in 

different directions. 

We can model the path loss in two different ways. First method 

is to find the values for ( ) PL d0 , n and S for the entire data set 

and only separating the data into implant-to-implant and 

implant to body surface sets. The second method is to consider 

separate sets of parameters (i.e. ( ) PL d0 , n and S) for deep 

tissue implant versus near-surface implant. Here, we have 
chosen the second method in order to gain some insight into 

possible differences between these two scenarios. 

 Figure 8 shows the scatter plot for the path loss as a 

function of TX-RX separation for deep tissue implant-toimplant scenarios described in the previous section. The mean 

value of the random path loss has been displayed by a solid 

line. This is obtained by fitting a least squares linear regression 

line through the scatter of measured path loss sample points in 

dB such that the root mean square deviation of sample points 

about the regression line is minimized. Random shadowing 

effects of the channel occur where the TX-RX separation is the 

same, but have different directions or positions with respect to 

each other. As shown in Fig. 9, this random variable has a 

normal distribution with zero mean and standard deviation σ s

i.e. ~ (0, )

2 S N σ s . 

Figure 8. Scatter plot of the path loss versus distance for deep tissue implant 

to another implant 

Figure 9. Distribution of the shadow fading for deep tissue implant to 

another implant 

Similarly, Fig. 10 and Fig. 11 represent the path loss scatter 

plot and probability density function of the shadow fading 

random variable for the deep tissue implant to body surface 

scenarios. A distance of up to 20 mm directly from the body 

surface has been considered in the definition of the body 

surface sample points. We also tried 2mm and 10mm 

distances and observed that the derived path loss parameters 

did not vary much. One should keep in mind that layers of 

clothing could cause additional loss to the signal. Plots 

corresponding to near surface implant scenarios have been 

omitted for brevity. 

Figure 10. Scatter plot of the path loss versus distance for deep tissue implant 

to body surface (within 20 mm of the skin) 

Figure 11. Distribution of the shadow fading for deep tissue implant to body 

surface (within 20 mm of the skin) 

Table 1 summarizes the extracted parameters for the statistical 

path loss models decribed above. 

Implant to Body 

Surface ( )( ) PL d0 dB n (dB) σ s

Deep Tissue 47.14 4.26 7.85 

Near Surface 49.81 4.22 6.81 

-25 -20 -15 -10 -5 0 5 10 15 20 25 0

0.01

0.02

0.03

0.04

0.05

0.06

dB

PDF 0 50 100 150 200 250 300 350 400 450 500 20

30

40

50

60

70

80

90

100

110

120

range (mm)

Path Loss (dB)

-30 -20 -10 0 10 20 30 0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0.05

dB

PDF 0 50 100 150 200 250 300 350 400 450 500 20

30

40

50

60

70

80

90

100

110

120

range (mm)

Path Loss (dB)
(a) 

Implant to 

Implant 

( )( ) PL d0 dB n (dB) σ s

Deep Tissue 35.04 6.26 8.18 

Near Surface 40.94 4.99 9.05 

(b) 

Table 1. Parameters for the statistical path loss model: (a) Implant to body 

surface channel (b) Implant to implant channel 

Deriving a channel model for communication between an 

implant and another node that resides further away from the 

body is more challenging. In the simple case of an 

environment where there are no objects or obstacles, a free 

space path loss can be added to the above models to account 

for the additional loss that the implant signal will go through 

once it leaves the body. On the other hand, if there are objects 

adjacent to the body in the surrounding environment, further 

loss could occur and the model needs to be adjusted 

accordingly. With appropriate assumptions this channel could 

be considered as an aggregate of two concatenated channels: 

implant to body surface and body surface to the external node 

channels. Further analysis and data is needed to investigate 

this issue and obtain the appropriate statistical models. 

V. CONCLUSION

In this paper, we have constructed an immersive visualization 

environment to conduct research in order to characterize RF 

propagation from medical implants. Extensive simulations 

have been performed to obtain a statistical path loss model for 

MICS channels. The model is based on four near surface and 

two deep tissue implant applications in a typical male human 

body. The authors recognize the fact that the extracted 

parameters for the statistical model are based on the simulation 

data; therefore, upon availability of measurement data from 

physical experiments, the results outlined in this paper should 

be further validated. However, no such reference data set is 

currently available. Along this line, efforts are underway to 

conduct and obtain body surface measurements which can be 

emulated in the immersive system for cross-verification 

purposes. 

The path loss models obtained in this study have been adopted 

by the IEEE802.15 task group TG6 on body area networks. 

The accuracy and applicability of such models requires further 

validation and investigation by scientists and engineers. 

The 3D visualization platform can also be used to study 

body surface propagation for wearable devices that operate at 

other frequency bands (e.g. ISM, UWB). It is also an ideal 

environment to observe and study the creeping wave 

propagation on the body surface resulting from wearable 

devices. We are currently studying this phenomenon and 

further information will be provided in a future publication. 

In general, studying a specific medical implant application with 

custom made antennas is also possible with this platform. More 

in-depth research on this subject is undoubtedly required to 

further understand the characteristics of radio frequency 

propagation from medical implants. The authors hope that the 

virtual reality environment introduced here would create a 

flexible platform where efficient collaboration between 

engineers and medical experts would become possible. 

ACKNOWLEDGMENT

The authors would like to express their gratitude to 

Nicholas Hirth and Ken Ferreira from Ansoft Corporation for 

their assistance and support in applying the body model to the 

3D immersive system. 

We would also like to thank Steven Satterfield of the 

Scientific Applications and Visualization Group for his 

assistance in system setup and his ongoing contribution in the 

development of tools for the NIST immersive visualization 

system. 

REFERENCES

[1] K.Y.Yazdandoost, R. Kohno, “An Antenna for Medical Implant 

Communication Systems”, Proceedings of the 37th European 

Microwave Conference, Oct. 2007, Munich, Germany 

[2] J. G. Hagedorn, J. P. Dunkers, S.G. Satterfield, A. P. Peskin, J. 

T. Kelso, J. E. Terrill, “Measurement tools for the immersive 

visualization environment: Steps toward the virtual laboratory”, 

Journal of research of the National Institute of Standard & 

Technology, Vol. 112, Num. 5, Sept.-Oct. 2007 

[3] G. Yang, “Body sensor networks”, Springer-Verlag London 

Limited 2006, ISBN 1-84628-272-1 

[4] P. S. Hall, Y. Hao, “Antennas and propagation for body centric 

wireless communications”, Artech House, Inc. 2006, ISBN 1-

58053-493-7 

[5] C. Polk, E. Postow, “Biological effects of electromagnetic 

fields”, CRC Press, Inc., 1996, ISBN 0-8493-0641-8 

[6] K.Y.Yazdandoost, R.Kohno, “Body Implanted Medical Device 

Communications”, IEICE Trans. Comm., Vol. E92-B, No. 2, 

Feb. 2009