On the Use of Knitted Antennas and Inductively Coupled RFID ...

IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, VOL. 10, NO. 6, DECEMBER 2016 1047

On the Use of Knitted Antennas and InductivelyCoupled RFID Tags for Wearable Applications

Damiano Patron, Member, IEEE, William Mongan, Member, IEEE,Timothy P. Kurzweg, Senior Member, IEEE, Adam Fontecchio, Senior Member, IEEE,

Genevieve Dion, Endla K. Anday, and Kapil R. Dandekar, Senior Member, IEEE

Abstract—Recent advancements in conductive yarns and fab-rication technologies offer exciting opportunities to design andknit seamless garments equipped with sensors for biomedicalapplications. In this paper, we discuss the design and applicationof a wearable strain sensor, which can be used for biomedical mon-itoring such as contraction, respiration, or limb movements. Thesystem takes advantage of the intensity variations of the backscat-tered power (RSSI) from an inductively-coupled RFID tag underphysical stretching. First, we describe the antenna design alongwith the modeling of the sheet impedance, which characterizes theconductive textile. Experimental results with custom fabricatedprototypes showed good agreement with the numerical simulationof input impedance and radiation pattern. Finally, the wearablesensor has been applied for infant breathing monitoring using amedical programmable mannequin. Amachine learning techniquehas been developed and applied to post-process the RSSI data,and the results show that breathing and non-breathing patternscan be successfully classified.

Index Terms—Antennas, biomedical communication, RFID tags,textile technology, wearable sensors.

I. INTRODUCTION

R ECENTLY, the integration of wireless smart devices intoclothing has revolutionized the monitoring of physical

activities and wellness. These garments incorporate physiolog-ical sensors and low-power computing units allowing for con-tinuous biomedical monitoring and activity tracking [1], [2]. Byadding low-power transceivers, such as Bluetooth Low Energydevices (BLE), the information can be conveniently transmitted

Manuscript received August 22, 2015; revised November 10, 2015; acceptedDecember 21, 2015. Date of publication April 25, 2016; date of current versionDecember 30, 2016. The research results were based upon work supported bythe National Science Foundation Partnerships for Innovation: Building Inno-vation Capacity (PFI:BIC) subprogram under Grant 1430212. This paper wasrecommended by Associate Editor P. Chiang.D. Patron, T. P. Kurzweg, A. Fontecchio, and K. R. Dandekar are with

the Department of Electrical and Computer Engineering, Drexel Univer-sity, Philadelphia, PA 19104 USA (e-mail: [email protected];[email protected]; [email protected]; [email protected]).W. Mongan is with the Department of Computer Science, Drexel University,

Philadelphia, PA 19104 USA (e-mail: [email protected]).G. Dion is with the Westphal College of Media Arts and Design, Drexel Uni-

versity, Philadelphia, PA 19104 USA (e-mail: [email protected]).E. K. Anday is with the College ofMedicine, Drexel University, Philadelphia,

PA 19104 USA (e-mail: [email protected]).Color versions of one or more of the figures in this paper are available online

at http://ieeexplore.ieee.org.Digital Object Identifier 10.1109/TBCAS.2016.2518871

to mobile devices. However, these devices typically use addi-tional dedicated transceiver modules and require batteries orother sources of energy.Radio-Frequency Identification (RFID) technology is being

increasingly utilized in an expanding set of applications. For tra-ditional metal-based tags, it has been shown that the backscat-tered power (RSSI) transmitted from a passive RFID tag can beused as metric for detecting material deformations [3]. In otherwords, when a strain force is applied on an RFID tag, the phys-ical deformation of the embedded antenna causes a shift of itsresonant frequency. Consequently, the RSSI variation receivedfrom the interrogator can be correlated to the mechanical defor-mation of the object where the tag is installed.In this paper, we propose to take advantage of this new ap-

plication of RFID technology, along with advances in knitting-based fabrication techniques, to realize comfortable and bat-tery-less wearable strain sensors. These sensors can be used formonitoring contractions, respiration patterns and limb move-ments. In other words, these sensors can be used for applicationswhere a mechanical movement causes stretching and changes inRSSI responses. As a result, the desired wearable wireless strainsensor should meet the following requirements: 1) comfortableto wear, 2) highly stretchable, 3) good impedance matching be-tween antenna and microchip, and 4) sufficient radiating char-acteristics to maintain communication under different levels ofphysical deformation.The feasibility of building electrical devices using conductive

fabrics has been analyzed through electrical characterizationof transmission lines [4]. Several papers have demonstratedmounted wearable transmission lines and antennas whereconductive fabrics are applied onto woven fabrics [5]. Previouswork shows conductive copper foil or fabrics bonded to aflexible substrate. These techniques show limitations in termsof electrical losses and bulkiness. We address these drawbacksby knitting conductive and non-conductive yarns in a singleprocess, resulting in smart textiles that are unobtrusively inte-grated into the host garment, eliminating the need for chemicaladhesives that degrade performance and decrease elasticity.The proposed wearable strain sensor is composed of a folded

dipole antenna, equipped with an inductively coupled RFID tag.As opposed to conventional microchips, the two small pads ofthe RFID chip package do not require physical soldering to theantenna arms as the energy is inductively transferred throughan internal matching circuit. The advantage of this inductively

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1048 IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, VOL. 10, NO. 6, DECEMBER 2016

coupled RFID technology is in maintaining the full flexibility ofthe fabric, even around the microchip area. Furthermore, undersufficiently large mechanical deformations, the RSSI variationwill be enhanced by the decoupling between the microchip andthe antenna’s arms.The ability to determine aberrations in the control of respi-

ration using RFID technology may have particular importancein detecting apnea, or cessation of respiration. Monitoringinfants at risk for respiratory events could be helpful in de-tecting apnea which, if not reversed in a timely manner, couldlead to cardiorespiratory arrest. Currently, the technology formonitoring infants at risk involves equipment that is connectedby cables attached to two electrodes on the baby’s chest underhis or her arms. As the baby’s chest moves during breathing,the impedance between the electrodes changes. If the monitordoes not detect changes in the electrodes triggered by a baby’sbreathing effort, a light will come on and an alarm will sound.However, these monitors are bulky, require frequent recharging,and are prone to signal interference that can occur from externalsources; interference could cause the monitor to miss apneawith resultant respiratory arrest. In the field of contactless res-piration sensors, in [20] the authors propose a system-on-chipUWB pulsed radar for respiratory rate monitoring. In [21] theauthors propose the design of an integrated circuit equippedwith a MEMS sensor for measuring the patient’s nasal air flow,which require an on-board cell battery.The wearable strain sensor described in this paper has the

potential for improving these current technologies since it canbe used to wirelessly detect breathing pattern with a comfort-able and battery-less garment, using commercial off-the-shelfcomponents. In order to evaluate the performance of the strainsensor for apnea monitoring, we have used a mechanical babymannequin which is used for medical training. This mannequin,called Laerdal SimBaby [13], can be programmed with specificbreathing and non-breathing intervals and the chest of the babyresembles the usual expansion of a breathing pattern. These in-tervals are classified using real-time statistical processing ap-proaches, including Support VectorMachines, FFT, and hypoth-esis testing, some of which have been used successfully in ana-lyzing electromyogram signals in the past [17]. In this paper weshow that the proposed sensor, along with amachine learning al-gorithm for post processing the RSSI data, is able to successfullyclassify the breathing or non-breathing patterns using a SupportVector Machine, classifying on statistical features of that RSSIdata as well as accurately estimate the rate of breathing of thesubject. In Fig. 1 we show a diagram of the full system com-prising the wearable sensor and the RFID receiving system.This paper is organized as follow: in Section II we discuss

the textile material selection and characterization, the RFIDtag, and the simulation of the folded dipole antenna. Section IIIdescribes the measurement results in terms of the antennainput impedance and radiation characteristics, and quantifieshuman body proximity effects on RFID sensor performance.In Section IV we analyze the system level performance whenthe sensor is employed for respiration monitoring. Finally,conclusions are drawn in Section V.

Fig. 1. Block diagram of the full system. The RFID reader interrogates thewearable sensor, which responds with RSSI values that vary based on the levelof stretching. The RSSI data are transferred to the computer for the machinelearning processing and final display of the breathing activity.

TABLE ISUPPORTING MATERIALS CHARACTERISTICS

II. WEARABLE RFID STRAIN SENSOR DESIGN

A. Material Selection and Characterization

The strain sensing application of the proposed knitted RFIDtag requires a high degree of flexibility under mechanical de-formation. For the non-conductive part of the garment, we em-ployed a mix of wool and lycra, which maintains the planarantenna architecture while ensuring high elasticity. The con-ductive layout was knitted with high conductive, silver-coatedyarns. As a substrate support for non-human body based labora-tory measurements, we used a 55 mm thick polyethylene foamhaving a dielectric constant close to air.For accurate simulations of the antenna design, we charac-

terized the non-conductive fabric as well as the foam supportthrough an Agilent 85070E Dielectric Probe Kit connected toa N5230A Vector Network Analyzer. The measurement esti-mates the dielectric constant, or complex permittivity

, as well as the . While the formermeasures how much energy is stored in a material, the latterexpresses how lossy or dissipative the material is. Table I sum-marizes the measured characteristics. The dielectric constant ofthe foam approximates the unitary value of the air, whereas thenon-conductive textile is slightly higher. For both materials, theloss tangent is relatively low.For knitting the conductive layout, we selected a 99% pure

silver plated nylon yarn having linear resistance equal to50 and 27% of maximum elongation. When a conductiveyarn is used, the finite conductivity of the planar structure isdetermined by the yarn’s linear resistance and knitting method.As the contact area between the conductive loops increases, theequivalent resistance of the fabric decreases. Furthermore, theconductivity is highly dependent on the direction of the currentflow in the textile, knitting geometry and loop density [6].


PATRON et al.: ON THE USE OF KNITTED ANTENNAS AND INDUCTIVELY COUPLED RFID TAGS FOR WEARABLE APPLICATIONS 1049

To ensure good conductivity of the overall design, the loopsforming the antenna are tightly knit, even when the fabric isin a relaxed state. In Fig. 5(c) we show a close-up picture ofthe knitted pattern. The conductive threads have been madedenser along the direction of maximum current flow, in order tominimize the conduction losses. The following antenna layoutselection and optimization takes into account the aforemen-tioned factors as well as the practical manufacturability usingconventional knitting machines for industrial production.

B. RFID Tag SelectionOne of themajor challenges of smart textiles is the integration

of lumped components, which require physical connection withthe conductive textile element. In the literature we can find sev-eral approaches that attempt to address this challenge [6]. How-ever, the use of conductive epoxy and other solid compoundstend to stiffen the structure and degrade the electrical perfor-mance.We addressed this limitation by utilizing a novel approach.

We selected the Murata MAGICSTRAP, a 2-port IC tag em-ploying inductive coupling technology [7], as an RFID tag forthe wearable strain sensor. Specifically, the two small pads ofthe SMD component allow coupling of the RF energy to thetwo antenna’s arms, avoiding the need for physical soldering.The component can be easily integrated into textiles by knittinga small pocket between the two antenna arms, thus maintainingthe full flexibility and comfort of the resulting garment. Further-more, this choice allows for enhancing the strain sensitivity tophysical deformation, as joint variations of input impedance andRF decoupling are significantly higher than radiation patternchanges alone [3]. In other words, by using the proposed RFIDtag, the physical deformation of the antenna causes impedancevariations and coupling reduction between the IC and the an-tenna, yielding significant variations of the backscattered power(RSSI).For the above reasons, we selected theMAGICSTRAPmodel

LMXS31ACNA-011, a small 3.2 1.6 mm SMD IC with inputimpedance equal to within the 870–915MHz frequency bandwidth. In Table II we summarize the mainfeatures of the RFID tag. The SMD package includes a stan-dard EPC global Class1Gen2 integrated circuit, along with amatching unit that allows the inductive coupling of energy be-tween the component’s pads and the knitted antenna.When con-sidering the practical washability of the final garment, the com-ponent can sustain up to 150 for 2 hours, which makes itsuitable for standard laundry processes. The high ESD (system-level electrostatic discharge) protection function makes it safeand compatible for wearable applications.

C. Knitted Antenna DesignTypically, the input impedance of RFID tags

does not match with the 50 standard which characterize themajority of front-end antennas. Due to the non-zero reactance

, antennas for RFID tags are properly designed for complexconjugate matching with these unconventional impedances. Forthe case of the MAGICSTRAP RFID tag, the input impedanceis characterized by a resistance and a reactance of

.

TABLE IIMAGICSTRAP IC CHARACTERISTICS

Because of the negative values of , the proposed antennatopology was selected to exhibit inductive reactance for com-plex conjugate matching and thus maximum energy delivery be-tween the microchip and the antenna. For this reason, we choseto design a folded dipole antenna, which has two major char-acteristics that align with system requirements. First, the loopstructure in the knitting process allows us to achieve positive re-actance for proper complex conjugate matching with the RFIDtag. Second, the simple planar layout makes the folded dipolearchitecture suitable for conventional knitting machines whileallowing alignment of the conductive thread along the directionof maximum current flow.When designing knitted or embroidered conductive struc-

tures, the layout cannot be modeled or assumed to be a pureconductive sheet. In our case, we can define knitting as theprocess of creating fabric with yarns by forming a series ofinterconnected loops. As suggested in [8], these structures ex-hibit higher electrical length compared to corresponding idealcopper sheets. For these reasons, the conductive layout shouldbe modeled by a sheet impedance comprising resistive andimaginary parts and expresses in While the resistanceaccounts for the ohmic losses, the imaginary part contributesto the antenna input reactance due to the electrotextile knittedloops.The antenna was modeled and simulated with the High

Frequency Structure Simulator (HFSS). The design is a foldeddipole with a thin slot whose width and length act as the maintuning parameters for impedance matching at 870 MHz. Thecomplex sheet impedance was determined through a seriesof parametric simulations and comparison with a measuredprototype. The best fit between measurements and simulationswas achieved when Fig. 2 showsthe 3D antenna model for numerical simulations, where theantenna layout is placed on top of the supporting polyethylenesubstrate. The outer dimension of the proposed antenna is

and , while the internal slotlength is and .Once the antenna impedance was properly tuned for the best

complex conjugate matching at 870 MHz, the power transmis-sion coefficient between the antenna and the RFID tag wascalculated from the simulated input impedance according to

(1)

where and is the simu-lated antenna input impedance and the microchip characteristic



Fig. 2. 3D Simulation model of the proposed wearable RFID sensor. Thesubstrate characteristics were defined based on the dielectric measurementsreported in Table I. As an RF excitation, a lumped port was set according to theRFID tag impedance .

impedance given in Table II, respectively. Next, the return loss,or , can be calculated by [9]

(2)

The equation editor in HFSS allows for a convenient real timeplotting of this custom output quantity.In Fig. 3(a) we show the simulation of the antenna’s input

impedance . At the desired center frequency of 870 MHz, thecomplex impedance is equal to . The re-turn loss , computed with (2), is depicted in Fig. 3(b) forthe same frequency sweep. We note that the real part of theinput impedance is higher than expected. This deviation is dueto the final-tuning performed for matching the sheet impedanceto the actual measured prototype. As a result, the simulated inputimpedance is a contribution of both the antenna geometry andthe complex sheet impedance used to model the conductive tex-tile. However, the shows a good level of matching, with avery small percentage of power reflected in band: 0.04–0.1%.The 10 dB return loss bandwidth is about 70 MHz, coveringwith good impedance matching the RFID chip frequency bandfrom 870 to 915 MHz. The relevant Industrial Scientific andMedical band (ISM) is 865–928 MHz.The radiation characteristic resembles the same omnidirec-

tional beam as a conventional dipole radiator. As shown inFig. 4, the maximum current intensity occurs along the twomajor edges, while it reaches lower intensity along the shortedges. The resulting beam will therefore be characterized bya uniform radiation around the plane normal to the feed port( plane) and two nulls along the major axis ( plane).In Fig. 4 we show the 3D beam with respect to the antennalayout. The maximum gain is about 0.8 dBi, which is lower withrespect to a copper-based design, due to the textile complexsheet impedance.

III. EXPERIMENTAL RESULTSWhen designing knitted antennas, the transition between the

theoretical model to the actual prototype requires some itera-tions to accomplish the desired layout. While the theoretical di-

Fig. 3. Simulated antenna input impedance. (a) Simulated real and imaginaryparts of antenna input impedance. (b) Numerical estimation of the return loss

using (2). The antenna layout has been optimized to yield good impedancematching within the whole frequency range of the RFID tag: 870–915 MHz.

Fig. 4. Simulated current distribution and radiation characteristics. The highestdensity of current distribution flows along the major axis, generating an omni-directional beam normal to the axis.

mensions are given in the metric units, the CAD software usedfor knitting machines defines the layout in terms of the number



Fig. 5. Experiment setup of S-parameters measurements. (a) Networkanalyzer with detail of the two-port fixture for balanced impedance measure-ments. (b) Knitted dipole antenna prototype with detail of the pocket for RFIDtag housing. (c) Close-up picture of the knitted pattern.

of yarns and loops. Once the desired knitted folded dipole an-tenna was manufactured, its input impedance and radiation pat-tern were characterized and compared to the expected numericalresults.

A. Input Impedance Measurements

The topology of folded dipole antennas is characterizedby balanced input impedance, while the conventional coaxiallines are unbalanced (quasi-TEM). This characteristic does notallow for conventional scattering parameters (S-parameters)characterization using the coaxial ports of a network analyzer.However, the antenna’s input impedance can still be deter-mined through a differential approach, with a proper coaxialfixture that considers the antenna to be a two-port network.Fig. 5 shows the experiment setup with network analyzer andtwo-port fixture, along with the prototype of the knitted foldeddipole antenna.In these measurements, the fixture was made by two

semi-rigid coaxial cables terminated with SMA connectors atone end. The two cables were aligned together by soldering theouter shields and the central conductors were connected to theknitted antenna by using conductive epoxy. The network an-alyzer was properly calibrated and the fixture’s length de-em-bedded through the port-extension function.The measured two-port complex S-parameters were loaded

into MATLAB for computing the antenna impedancethrough the following expression [10]:

(3)

where is the 50 characteristic impedance of the networkanalyzer. The antenna can reasonably be assumed to be sym-metrically balanced, therefore in (3) we assume and

. After determining the antenna’s impedance , thereturn loss was calculated using (2).

Fig. 6. Measured antenna input impedance. (a) Real and imaginary parts ofinput impedance using the differential measurement approach. (b) Extraction ofthe return loss using (2). The 10 dB return loss bandwidth is 100 MHz.

In Fig. 6(a) the measurements show that at the desiredcenter frequency of 870 MHz, the impedance is equal to

, which yield a good conjugate matchingwith the RFID tag impedance . The respective return loss inFig. 6(b) exhibits a 10 dB bandwidth of 100 MHz, coveringthe desired frequency range. Through comparison with thesimulated return loss, we notice that the measurement returns alarger 10 dB bandwidth. This is potentially due to the lower Qfactor of the manufactured knitted radiator, which tends to bemore lossy, which in turn yields a larger bandwidth.Generally speaking, the Q factor is the ratio between the en-

ergy stored in the reactive field and the energy radiated. Its valueis determined by the conductor thickness, and the conductionlosses are proportional to the conductor thickness. A higher con-ductor thickness leads to higher conduction losses, lower Q andthus larger bandwidth. For modeling the conductive textile weused a 2D complex sheet impedance, but the actual prototypepresents a finite textile thickness for which this planar modelcannot account. As a verification, we re-simulated the antennaassuming a higher resistive part of the sheet impedance . Wehave seen that for every 0.05 of incremental value of



Fig. 7. Anechoic chamber setup for radiation pattern measurements.(a) Directional antenna connected to Impinj Speedway reader and knittedfolded dipole antenna with MAGICSTRAP tag attached to the 3D manipulator.(b) Resulting radiation pattern.

the bandwidth increases by approximately 15%, but at the sametime the resonant frequency is shifted slightly upward. There-fore, the complex sheet impedance is a very useful tool for mod-eling textile RF structures, but it also shows some trade-offsbetween resonant frequency and bandwidth due to the lack ofa third dimension (thickness). Fortunately, the UHF RFID fre-quency band (860–915 MHz) is relatively narrow and both thesimulated and the measured cover the entire bandwidth withgood impedance matching.

B. Radiation Pattern Measurement

Evaluating the directivity of the knitted antenna allows usto infer the current distribution occurring along the conduc-tive threads. To this end, the knitted folded dipole prototypeequipped with the MAGICSTRAP RFID tag, and an ImpinjSpeedway reader [12], were set 70 cm apart within an anechoicchamber. The antenna was attached to a 3D manipulator for az-imuth and elevation plane rotations. The rotations occurred insteps of 5 , and 200 RSSI values were averaged for each an-gular step. Fig. 7 shows the anechoic chamber setup along withthe resulting azimuth and elevation planes. The knitted foldeddipole antenna offers the expected omnidirectional radiation inthe elevation plane, and the two nulls in the azimuth plane corre-spond to the location of the antenna’s short edges. This is in goodagreement with the predicted radiation characteristics of Fig. 4.The high conductive thread density along the longer edges al-lows us to support the expected current flow and generation ofthe desired radiation pattern.

C. Consideration on the Human Body Proximity

The proposed wearable RFID strain sensor aims to determinehuman functions such as breathing/contraction patterns or limbmovements. Therefore, it is important to evaluate human bodyeffects on antenna performance due to the absorption of radia-tion between tissue layers.HFSS along with the 3D human body model [11], were used

to simulate the antenna at different heights above the humanstomach to determine loading effects. We observed that the res-onant frequency is progressively downshifted and attenuatedwhen the sensor approaches the human skin. As shown in Fig. 8,at the center frequency of 870 MHz, the falls below the6 dB threshold when the distance is between

. However, the optimal regime under 10 dB is

Fig. 8. Return loss of the RFID tag antenna for different distances from thehuman stomach model.

Fig. 9. Radiation patterns as function of the heights above the human chest,at the frequency of 870 MHz. (a) . (b) . (c) .(d) . Under low distance the gains are negative, while when isincreased, the gains are about 60% higher than without human chest.

achieved when . This analysis allows for perfor-mance optimization under these loading effects. In fact, the an-tenna can be tuned to resonate 30–40 MHz above the desiredcenter frequency and a fabric spacer of 5–10 mm can be addedbehind it in order to maintain good impedance matching whenworn.The radiation pattern also changed significantly in the prox-

imity of the human body. Because of absorption and reflectionof the tissue layers, the radiation is directed broadside, normalto the antenna’s plane. As depicted in Fig. 9, when the distanceis less than 10 mm, the antenna’s efficiency degrades signif-

icantly, causing negative gains. When the antennaexhibits directional beams with positive gain, up to 3.4 dBi. TheHalf-Power Beamwidth (HPBW) is comparable to the omnidi-rectional beam showed in Fig. 4. Non-conductive fabric spacerscan be knitted with the final garment for achieving the desireddistance between the antenna and the skin.If the antenna becomes wet due to sweat the functionality of

the sensor would be altered due to the conductive characteristicof water. For this reason, the fabric spacer mentioned above willalso act as a protective padded layer keeping the antenna awayfrom the skin. This additional layer would protect the sensorpreserving the elasticity of the antenna.



IV. APPLICATION AS A WEARABLE BREATHING MONITOR

Since the aim of this sensor is to capture contractions or respi-ration of a human body, we have performed a preliminary strainsensing analysis using a medical mannequin used for medicaltraining. Specifically, we focused our attention to the applica-tion of the sensor as a breathing monitoring system.Monitoring if a child is breathing regularly can be helpful

in detecting apnea with the possibility to prevent an acutecardiorespiratory arrest. Currently, wearable baby monitors aremade from a baby onesie equipped with a WiFi or Bluetoothmodule that collects data from sensors that are integratedinto the garment, and sends the data to a mobile device [2].However, these monitors employ active modules that are bulkyand need to be recharged regularly. The wearable strain sensordescribed in this section has the potential of improving thiscurrent technology since it can be used to detect the breathingpattern of a child through a more comfortable, and fully textilegarment that does not require batteries or cumbersome elec-tronics.

A. Experiment System Setup

In order to evaluate the performance of the strain sensor forrespiration monitoring, we have used a mechanical baby man-nequin, which is used for medical training, for testing the de-vice. This mannequin, called Laerdal SimBaby [13], can beprogrammed with specific breathing and non-breathing inter-vals and the chest of the baby resembles the usual expansionof a breathing pattern. The wearable strain sensor was fittedaround the baby mannequin’s chest, and the Impinj SpeedwayRFID reader [12] was placed at about 1 m from the mannequin.The minimum distance required by the FCC is 20 cm [19].Five minutes of continuous data was collected from the pro-grammable infant mannequin device as follows: breathing from0–60 seconds, non-breathing from 60–100 seconds, breathingfrom 100–210 seconds, non-breathing from 210–240 seconds,breathing from 240–300 seconds. To remove noise and miti-gate quantization, a Gaussian filter was applied to the RFIDdata collected. The algorithm considered for determining ac-tivity state is the Support Vector Machine (SVM) [14]. This al-gorithm is a machine learningmodel that analyzes data to recog-nize patterns used for classification analysis. This analysis sepa-rates the data points collected by the RFID interrogator into twoclasses: in this case, breathing and non-breathing classes. Thedata points are arranged into a 2D plot with standard deviationon the -axis and RSSI mean on the -axis. In Fig. 10, the exper-imental system setup comprising the SimBaby mannequin andcontrolling system, along with the proposed wearable sensorand the RFID interrogator are shown. The in-terrogator antenna is placed at from the simbaby. TheRFID reader output power is set to to complywith the maximum EIRP power of 36 dBm as specified by theFCC Part 15.247 regulations. The power density at the simbabyposition can be calculated as

(4)

Fig. 10. Experimental setup for baby breathing monitoring. The SimBabymannequin is controlled by a programmable actuator that expands the baby’schest with specific breathing rates. The wearable RFID sensor, worn aroundthe baby’s chest, is interrogated by the RFID unit in front of it.

The effective area of the wearable sensor isas shown in Fig. 2. Therefore, the power received from

the wearable sensor is

(5)

Since the impedance match between the RFID tag and the an-tenna is relatively high we can assume ideal efficiency, whichmeans that the back-scattered power equals the received power.The gain of the wearable RFID sensor is ,which yield an Equivalent Isotopically Radiated Power of

(6)

B. Experimental ResultsTo classify the data into breathing and non-breathing states,

the statistical features of windows of data points are arrangedinto space, and a line, curve, or plane is computed that “best”divides the points. There can be many ways to separate the data,and there can be no possible way to cleanly separate the dataif the data points are not well-separable. The degree of separa-bility of data points into classes is provided by metrics, such asthe Fisher Linear Discriminant ratio function (FDR) [15], whichwas employed for this study using mean and standard devia-tion and defined as follows:

(7)

Using this method, it was found that, when arranging the RSSIvalues collected into temporal windows, the mean and standarddeviation of the data within each window yield well-separablefeatures, which was confirmed by the performance of our exper-imental study, discussed later in this section. Because the meanand standard deviation were found to be highly separable, theyplot in such a way that one or more separating “lines” can bedrawn that separates most of the data points. The “best” suchseparating line is the one that separates the groups of points andalso maximizes the distance or margin between the line and the



nearest data point from each class. This way, new data points canbe classified with a low risk of error due to conflating the twostates by virtue of the separating line being drawn too closelyto the boundary of either class. Other data elements, such asthe Doppler and phase angle from the RFID interrogator, wereconsidered, as well as other statistical features of the RSSI, butthese were not predicted by the FDR to be as linearly separableand thus not predicted to be conducive to real-time predictiveanalysis.To determine the separability of features via the FDR metric,

it is necessary to compute features onwindows of both breathingand non-breathing classes of data. It is not possible to collecttraining data in the non-breathing class, since the test subjectwould have to cease respiratory activity for purposes of datatraining, though this is feasible in simulation for purposes of de-termining which features are most suitable for deployment. Inother applications, such as uterine monitoring or motion moni-toring, training is feasible in both classes, so Two-Class analysisremains valuable; however, results from Two-Class classifiersare compared against single-class anomaly detectors in order tocompare the performance of a single-class approach when it isneeded.Using the mean and standard deviation as the base features

in this study, the FDR was computed across the same simulateddata but with different window sizes: 128, 64, 32, and 16-ele-ment windows. For the approximately 30 Hz RFID interrogatorbeing used, this corresponds to time windows of 4.27, 2.13,1.07, and 0.53 seconds, respectively. Experimental results haveshown that the FDR separability improves as the window sizeincreases, as shown in Fig. 11.Some related efforts do exist that did not indicate detection

time or real-time detection accuracy. However, like our work, itis noted that improved signal to noise performance is observedwhen the window size is increased [22]. Two measurements aretaken on the chest wall and the abdomen, and the phases of thesinusoidal signals are compared to classify breathing state. Weare using statistical features of a single placement currently inorder to measure detection accuracy against known conditions(pre-programmed into the SimBaby), but a second chip place-ment on the body is feasible on the proposed textile band. Afeature window length of 10-seconds was identified in anotherwork, which is approximately what we have found to yield op-timal results as well [23]; this work utilizes the ECG signal asopposed to our passive RFID, and assuming similar detectionaccuracies, a passive RFID signal appears to be a viable alter-native.Using this information, a web service was written to interro-

gate the RFID tag and gather raw data about its state, includingits RSSI value. Initially, this service runs on the interrogatorprior to any statistical inference, so that several seconds oftraining data can be obtained in order to establish a baseline ofthe window features under normal conditions for the subject.For example, the subject may breathe normally during thistime, and the web service will store the statistical featurescorresponding to that state during the training phase. This webservice is queried by a client periodically to classify subjectstate from windows of that data. This service computes whetherthe current window’s features lie on the normal (breathing) or

Fig. 11. Experimental results of the breathing monitoring test. The supportvector machine has been used to separate the breathing and non-breathing pat-terns from the collected RSSI data. (a) Plot generated using a window size of 64.(b) Plot generated using a window size of 128. The larger window size allowsa better separation of the two clusters.

non-normal (non-breathing) side of the support vector spacecomputed during training. Additionally, it runs a hypothesis testto determine whether this window’s data is likely comprisedonly of zero-mean noise, indicating a non-breathing state. Thisadditional information can help augment in cases when thesupport vector computation lies within the margin or closeto the boundary separating the breathing and non-breathingclasses. Other biological data classification efforts have usedstatistical features such as the mean, standard deviation, kur-tosis, and others [18]; the features chosen for this study, basedon the separability predicted by the FDR, include the windowmean, window standard deviation, and the p-value of the t-testusing a 0-mean window hypothesis. Varying the window sizeand training set size, as well as utilization of hypothesis testingresults to inform the support vector machine improves classi-fication accuracy to approximately 77% in this study. Theseservices were deployed in a real-time monitoring prototype thatdetected breathing cessation in 8 seconds, and a restoration ofbreathing in 5 seconds.The results shown in Fig. 11 show that the wearable RFID

sensor along with the Support Vector Machine algorithm



separates the breathing and non-breathing patterns. The hori-zontal separation of points in the breathing and non-breathingclasses indicates that the RSSI window mean better informs thestate prediction of the subject, due to the overlap between thestandard deviation of windows across both classes. Althoughthe RSSI standard deviation feature is somewhat separable, ityields more overlap than the RSSI mean. This is visualized asthe upward-sloping separating line in Fig. 11, and results fromthe misclassified points “pulling” the line upwards towardsthose points. As a result, this feature is not conducive to ahyperplane approach such as a support vector machine like themean, and instead is more conducive for use in an ensembleapproach using nearest neighbor or k-means clustering, whichis the subject of our current work. The few points that belong tothe wrong cluster are errors due to the mannequin transitioningfrom breathing to non-breathing states, and vice versa.

V. CONCLUSION

In this paper we discussed the design and application of aknitted folded dipole antenna equipped with an inductively cou-pled RFID chip for the development of a wireless, wearablesensor. The aim of the knitted passive sensor is for monitoringrespirators or other body movements through a comfortable andbattery-less wireless sensor. The unique feature of this workis the development of knitted antennas embedded in the hostgarment and equipped with inductively-coupled RFID tags forbackscatter power sensing. We developed statistical analysisand signal processing algorithms to interpret and classify thisdata to determine the wearer’s state. Through the use of com-mercially available RFID readers, the wearable RFID sensorresponds to mechanical deformations with backscattered powervariations (RSSI). The successive filtering and analysis allowsinference about the biomedical parameter that is being moni-tored.For developing this sensor, first we have designed and tested

a folded dipole antenna tuned for impedance matching with aninductively coupled RFID tag. Specifically, we have applied thecomplex sheet impedance numerical condition in order to ac-count for the actual losses and parasitic effects introduced by theknitted conductive yarns. The input impedance measurementsshowed good agreement with the numerical simulation, and theradiation pattern resembles the predicted directivity typical of adipole antenna.Second, we have performed an experiment for validating the

sensor as a wearable breathing monitor. A programmable med-ical baby mannequin has been set with specific breathing andnon-breathing intervals, with the chest of the baby expanding asan actual breathing movement. The wearable strain sensor wasfitted around the baby’s chest, and a commercial RFID readerwas used to collect the RSSI data. Next, a machine learningtechnique has been used for post-processing the data and it hasshown that the two breathing and non-breathing patterns can besuccessfully separated.According to the results of this work, we demonstrated that

fully knitted antennas equipped with inductively coupled RFID

tags provide good strain sensing performance to be used as wire-less sensors for monitoring biomedical parameters.

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Damiano Patron (S’11–M’15) received the B.S. de-gree in electronics engineering from the University ofPadua, Padua, Italy, in 2010, and the M.S. and Ph.D.degrees in electrical and computer engineering fromDrexel University, Philadelphia, PA, USA, in 2013and 2015, respectively.From 2002 to 2010, he worked with Euro-Link

S.r.l. as a System Integrator of RFID Systems. In2010, he joined the start-up company Adant Inc. asan RF and Antenna Engineer, designing reconfig-urable antennas for WiFi and RFID applications.

Currently, he is working as a Staff Scientist for Witricity Corporation on newwireless charging technologies. His Ph.D. research focused on the design ofreconfigurable antennas for throughput maximization and DoA estimation inwireless networks. He also worked on the development of wearable sensorsand power harvesting systems. He is the author and coauthor of severalpatents, and scientific papers in the field of reconfigurable antennas, antennasminiaturization and wearable technologies.Dr. Patron received several awards, including the Young Scientist Best Paper

Award at the ICEAA IEEE APWC EMS Conference in 2013, and the first prizefor the Rectenna Spinout Contest at the 9th Annual IEEE International Confer-ence on RFID in 2015.

WilliamMongan (M’05) received the B.S. and M.S.degrees in computer science, and the M.S. degreein science of instruction from Drexel University,Philadelphia, PA, USA, in 2005, 2008, and 2008,respectively.From 2003 to 2008, he was a Researcher in the

Software Engineering Research Group (SERG) atDrexel University, where he worked in programcomprehension and service-oriented architectures.He serves as an Associate Teaching Professor andAssociate Department Head for Undergraduate

Affairs in the Department of Computer Science at Drexel University, where hisresearch focus is signal processing and data analytics on real-time biofeedback.Mr. Mongan is a Senior Member of the Association for Computing Ma-

chinery (ACM).

Timothy P. Kurzweg (S’92–M’95–SM’09) re-ceived the B.S. degree from Pennsylvania StateUniversity, University Park, PA, USA, in 1994, andthe M.S. and Ph.D. degrees from the University ofPittsburgh, Pittsburgh, PA, USA, in 1997 and 2002,respectively, all in electrical engineering.Currently, he is an Associate Professor in the

Department of Electrical and Computer Engineering,Drexel University, Philadelphia, PA, USA. Hisresearch interests include programmable imagingwith optical MEMS, precancerous detection using

white-light spectroscopy, diffuse optical communication using space-timecoding, and non-traditional electronics and antennas.Dr. Kurzweg is a member of the IEEE Lasers and Electro-Optics Society, the

International Society for Optical Engineers, and the Optical Society of America.

Adam Fontecchio (SM’11) received the B.A.,M.Sc., and Ph.D. degrees in physics from BrownUniversity, Providence, RI, in 1996, 1998, and 2002,respectively.Currently, he is a Professor in the Electrical and

Computer Engineering Department, Drexel Univer-sity, Philadelphia, PA, USA, and is Vice-Dean of theGraduate College. He is the inaugural Director ofthe Center for the Advancement of STEM Teachingand Learning Excellence (CASTLE), serving asVice-Chair of the IEEE Philadelphia Section, and is

a member of the IEEE-USA K-12 STEM Literacy Committee. His research

focuses on the area of nanophotonics. He has served as PI or Co-PI on morethan $18 million in sponsored research funding, and has authored over 90peer-reviewed papers.Dr. Fontecchio was selected as the 2015 Delaware Valley Engineer of the

Year. He was also the recipient of a NASA New Investigator Award, the DrexelGraduate Student Association Outstanding Mentor Award, the Drexel Uni-versity ECE Outstanding Research Achievement Award, and the InternationalLiquid Crystal Society Multimedia Prize.

Genevieve Dion received the B.A. degree in indus-trial arts from San Francisco State University, SanFrancisco, CA, USA, and theMaster of Industrial De-sign degree fromUniversity of the Arts, Philadelphia,PA, USA.Currently, she is an Associate Professor in the

Department of Design at the Westphal Collegeof Media Arts and Design, Drexel University,Philadelphia, PA, USA. Her research uses advancedmanufacturing processes for the flexible productionof mass-customizable smart textiles, including the

development of seamlessly knitted electronic yarns into “Garment Devices.”She believes that design plays an important role at the onset of the scientificprocess and brings this unique approach to her multidisciplinary researchlaboratory. During her tenure at Drexel University, she spearheaded interdisci-plinary research projects utilizing digital knitting machines as research tools. In2012, she founded the Shima Seiki Haute Tech Lab at the ExCITe Center. Herwork on permanently pleated silk has been in the collections of the Victoria andAlbert Museum in London (2003) and the DeYoung Museum in San Francisco(2010). She is an advisor to an NSF Fellow in Materials Science and advised aformer Department of Defense Fellow in Materials Science.Ms. Dion was named one of Fast Company’s MCP1000 and Top 100 Most

Creative People in Business in 2014. She was appointed as “Innovation Ambas-sador” to the United Nations’World Summit on Innovation & Entrepreneurship.

Endla K. Anday received the Medical Degree fromthe Medical College of Pennsylvania, Philadelphia,PA, USA, in 1973.She completed a residency in Pediatrics at the Chil-

dren’s Hospital of Philadelphia in 1975 and Fellow-ship training in Neonatal-Perinatal Medicine in 1978.Following a year of fellowship in pulmonary physi-ology in the Department of Physiology, University ofPennsylvania, Philadelphia, she was appointed As-sistant Professor of Pediatrics in the Department ofPediatrics at the Children’s Hospital of Philadelphia,

University of Pennsylvania School of Medicine, Section of Neonatal-PerinatalMedicine. In addition to clinical medicine, she received numerous grants to in-vestigate themechanisms of brain injury following hypoxic-ischemic insults, ef-fect of cocaine in the brain of a neonatal animal model and the effect of maternalhigh fat diet in the fetus on the development of adult-onset metabolic syndrome.She participated in a number of national clinical trials, including the effect ofexogenous surfactant on the lung of the premature newborn, the effect of thy-roid-stimulating hormone on lung biochemical maturation, the efficacy, safetyand tolerance of oral Zidovudine (ZDV) in HIV-infected pregnant women andtheir infants and the efficacy of recombinant human granulocyte-macrophagecolony stimulating factor for prophylaxis of neonatal nosocomial infection. In1996, she was recruited to St. Christopher’s Hospital for Children in the roleof Regional Clinical Director for Neonatal Services. She was appointed to Pro-fessor of Pediatrics and currently serves as the Medical Director of the NeonatalIntensive Care Unit at Hahnemann University Hospital, Philadelphia. She hasmore than 80 first author papers and chapters in books.Dr. Anday has served on numerous committees in the Academy of Pediatrics,

Section of Neonatal-Perinatal Medicine and is on the Editorial Board of theNeoReviewsPlus. She was selected for “Best Doctors in America” and “TopPediatricians in America” and holds membership in several prestigious societiesincluding: Society for Pediatric Research, American Pediatric Society, The NewYork Academy of Sciences, and Society for Gynecologic Investigation. She isalso a member of the Sigma Xi Society, Alpha Omega Alpha.



Kapil R. Dandekar (S’95–M’01–SM’07) receivedthe B.S. degree in electrical engineering from theUniversity of Virginia, Charlottesville, VA, USA, in1997, and the M.S. and Ph.D. degrees in electricaland computer engineering from the University ofTexas at Austin, Austin, TX, USA, in 1998 and2001, respectively.He worked at the U.S. Naval Observatory in

1992 and at the U.S. Naval Research Laboratoryfrom 1993–1997. In 2001, he joined the Electricaland Computer Engineering Department at Drexel

University, Philadelphia, PA, USA. Currently at Drexel University, he is a

Professor in Electrical and Computer Engineering; the Director of the DrexelWireless Systems Laboratory (DWSL); and Associate Dean for Research andGraduate Studies in the Drexel University College of Engineering. DWSL hasbeen supported by the U.S. National Science Foundation, Army CERDEC,National Security Agency, Office of Naval Research, and private industry. Hisresearch interests and publications involve wireless, ultrasonic, and opticalcommunications, reconfigurable antennas, and smart textiles. Intellectualproperty from DWSL has been licensed by external companies for commer-cialization.Dr. Dandekar is a former member of the IEEE Educational Activities Board

and cofounder of the EPICS-in-IEEE program.


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