06965590 personal lung function

2238 IEEE SENSORS JOURNAL, VOL. 15, NO. 4, APRIL 2015

Personal Lung Function Monitoring Devicesfor Asthma Patients

Alice M. Kwan†, Alexander G. Fung†, Peter A. Jansen, Michael Schivo, Nicholas J. Kenyon,Jean-Pierre Delplanque, and Cristina E. Davis

Abstract— Asthma affects over 300 million people worldwide.Asthmatics experience difficulty in breathing and airflowobstruction caused by inflammation and constriction of theairways. Home monitoring of lung function is the preferredcourse of action to give physicians and asthma patients a chanceto control the disease jointly. Thus, it is important to developaccurate and efficient asthma monitoring devices that are easyfor patients to use. While classic spirometry is currently thebest way to capture a complete picture of airflow obstructionand lung function, the machines are bulky and generallyrequire supervision. Portable peak flow meters are availablebut are inconvenient to use. There also exist no portableinexpensive exhaled breath biomarker devices commerciallyavailable to simultaneously measure concentrations of multiplechemical biomarkers. We have created a user-friendly, accurate,and portable external mobile device accessory that collectsspirometry, peak expiratory flow, exhaled nitric oxide, carbonmonoxide, and oxygen concentration information from patientsafter two breath maneuvers. We have also developed a softwareapplication that records and stores the gathered test informationand e-mails the results to a physician. Telemetric capabilitieshelp physicians to track asthma symptoms and lung functionover time, which allow physicians the opportunity to makeappropriate changes in a patient’s medication regimen morequickly.

Index Terms— Spirometry, peak expiratory flow (PEF),chemical biomarker, personal mobile devices, smart devices,breath analysis.

Manuscript received December 18, 2013; revised May 30, 2013 and Octo-ber 10, 2014; accepted November 7, 2014. Date of publication November 24,2014; date of current version February 5, 2015. This work was generouslyand partially supported by several funding agencies. The content of thiswork is solely the responsibility of the authors and does not necessarilyrepresent the official view of these agencies. Partial support is acknowledgedfrom: UL1 RR024146 #TR00002 from the National Center for ResearchResources (NCRR), a component of the National Institutes of Health (NIH),and NIH Roadmap for Medical Research [JPD, CED, NJK]; NIH #HL105573 [NJK]; The Hartwell Foundation [CED, NJK, JPD]; NIH #T32-HL007013 and #T32-ES007059 [MS]; UC Davis School of Medicine andNIH #8KL2TR000134-07 K12 mentored training award [MS]; AmericanAssociation for University Women (AAUW) Selected Professions Fellowshipsupport [AMK]. The associate editor coordinating the review of this paperand approving it for publication was Dr. Patrick Ruther. († Alice M. Kwanand Alexander G. Fung contributed equally to this work.) (Correspondingauthor: Cristina E. Davis.)

A. M. Kwan, A. G. Fung, J.-P. Delplanque, and C. E. Davis are withthe Department of Mechanical and Aerospace Engineering, University ofCalifornia at Davis, Davis, CA 95616 USA (e-mail: [email protected]).

P. A. Jansen is with the Product Development, Scanadu, Inc., Moffett Field,CA 94035 USA.

M. Schivo and N. J. Kenyon are with the Division of Pulmonary and CriticalCare Medicine, University of California at Davis, Davis, CA 95616 USA.

Digital Object Identifier 10.1109/JSEN.2014.2373134

I. INTRODUCTION

ASTHMA is a chronic pulmonary inflammatory diseasethat affects the airways, and is characterized by

an increased sensitivity to various stimuli. Subsequentstimulation may prompt the airways to narrow and induceproduction of mucus causing less air to flow into the lungs.Common symptoms of asthma include wheezing, shortness ofbreath, and chest tightness. The intensity of an acute asthmaexacerbation, also known as an asthma attack, is unpredictableand has the potential to be life threatening. While there aremedical treatments available to alleviate asthma symptoms,there is no cure [1].

As of 2004, approximately 300 million people worldwidewere afflicted with asthma [2]. In 2010, 25.7 millionindividuals were estimated to have asthma in the UnitedStates [3]. Complications due to asthma accounted for1.7 million emergency room visits in the United Statesin 2006 [4], about 14.2 million lost work days in adultsin 2008, and annual total cost to society of nearly $56 billiondollars [5]. More than 5 million children have asthma and theprevalence of asthma is greater than 15% for children livingin low-income families in the United States [6].

The severity of symptoms, triggers, and responsiveness totreatment medication are often unique to each individual.Thus, a comprehensive guideline for an asthma action planrecommends focusing on monitoring asthma symptoms asa goal for asthma therapy [7]. Spirometry, peak expiratoryflow measurement, and a non-invasive marker of airwayinflammation known as fractional exhaled nitric oxide (FeNO)are now used by health care professionals for diagnosis andmonitoring [8].

A spirometry test is a physiological test normally performedunder the supervision of trained professionals. It measures thevolume and flow rate of air that can be inhaled and exhaled,and is useful in describing the disease state in the lungs, assess-ing therapeutic intervention, and/or monitoring for adversereactions to medication. Two of the most important parametersobtained in a spirometry test are the forced vital capac-ity (FVC), described as the volume delivered during expirationwhen made as forcefully and completely as possible startingfrom full inspiration, and the forced expiratory volume in onesecond (FEV1), which is the volume delivered in the first sec-ond of the FVC maneuver [9]. Prior published work has shownthat the forced expiratory volume in six seconds (FEV6) takenduring a spirometry maneuver is an acceptable substitute for

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KWAN et al.: PERSONAL LUNG FUNCTION MONITORING DEVICES FOR ASTHMA PATIENTS 2239

FVC [10]. The results of a spirometry test are presented usinga spirometry graph which maps flow rate (L/s) over exhaledvolume (L).

Peak expiratory flow (PEF) has been established as an accu-rate, repeatable, and non-invasive test for monitoring airflowat home [11,12]. PEF is the maximum flow rate of expiration,which correlates to the degree of obstruction in the airways.Prescribed asthma monitoring plans with PEF monitoringhave been shown to decrease the number of severe asthmaepisodes [13], but are ineffective when not adhered to [14,15].This noncompliance may be due to the time and disciplinerequired to manually assess asthma symptoms over a longtime frame [16]. In addition, biomarkers like nitric oxide (NO)and carbon monoxide (CO) can give health care professionalsanother potential tool to help evaluate and determine asthmatreatment [17].

Portable technology focusing on full-body physiologicalmonitoring using sensors and mobile devices is becomingincreasingly prevalent. Recent research in this area has focusedon monitoring various physiological functions such as sweatrates [18], cardiac function [19], sleep [20], and biomarkersproduced during exercise [21]. These devices provide patientswith an inexpensive, portable, and convenient method to non-invasively measure biomarkers of body function to improvehealthcare.

The aim of this paper is to report a novel portable approachto asthma monitoring. An asthma monitoring device wasdeveloped that combines spirometry (FEV1, FEV6, andspirometry graph), PEF, and chemical breath biomarkermeasurements of nitric oxide (NO), carbon monoxide (CO),and oxygen (O2) into two breath maneuvers. A softwareapplication for Android mobile technology was developedto interpret and display relevant data, record the data toa file on the Android device, and e-mail the data fileto a health care professional for personalized care. Thispaper outlines the development of the instrumentation andsoftware application that enables portable and inexpensivereal-time collection of lung function parameters. Futureversions of these platforms may be particularly appropriatefor pediatric patients who may have greater difficultydocumenting their asthma symptoms during the course ofa day.

II. MATERIALS AND METHODS

A. Design and System Construction of the PortableAsthma Monitoring Device

1) Overview of the Device Layout: A novel portable asthmamonitoring device was created to provide the capability todetect several critical lung function parameters and recordthe data to a mobile device (Fig. 1). Patients exhale intoa flow chamber with embedded sensors that are tetheredto a smart device for data capture. A microcontroller andUSB host shield are used to digitize and send the sensorsignals to a mobile device through a standard USB connection.A custom software application on the mobile device processesthe signal to communicate relevant physiological informationback to the patient and allow the processed data to be easilyshared via telemetry.

Fig. 1. (a) Schematic overview of the asthma monitoring device and its com-ponents. (b) The portable asthma monitoring device with an Android MotorolaXoom tablet. (c) The hardware components of the asthma monitoring device.

2) Flow Chamber Specifications: To measure the flow rateof exhaled breath, a thin plate orifice volume flow meter [22]was tailored for the expected pulmonary flow rates (Fig. 2(a)).This type of volume flow meter allows an instantaneousflow rate measurement in a pipe and follows the Bernoulliobstruction theory, which describes the properties of a flowthat is forced by an obstruction from a duct of diameter Dinto a smaller flow passage of diameter d . A relationshipbetween the pressure drop and volume flow rate is obtainedfrom Bernoulli’s equation:

Q = Co Ao

√2(p1 − p2)

ρ(1 − β4)(1)

β = d

D(2)


Fig. 2. (a) Diagram of the flow chamber, (b) Computer-aided design drawingof the flow chamber and its dimensions.

where Ao is the area of the hole in the orifice plate, p1 isthe pressure upstream of the orifice plate, p2 is the pressuredownstream of the orifice plate, ρ is the density of exhaledbreath, β is the diameter ratio as shown in Equation 2, andCo is the orifice discharge coefficient which is typically on theorder of 0.6. The orifice discharge coefficient is a function ofthe Reynolds number and β [23]. The variable h is the lengthof the orifice plate between diameters D and d . The chemicalsensors were placed at least 8 times the value of h awayfrom the orifice plate to ensure that exhaled breath would flowover the chemical sensors [24]. In the device, the parametervalues were: D = 28 mm, d = 14 mm, h = 7 mm, andP1, P2, A, B, and C indicate the location of pressure sensors,NO sensor, CO sensor, and O2 sensor, respectively (Fig. 2(a)).

The flow chamber was constructed out of acrylonitrile buta-diene styrene (ABS) using a Stratasys FDM rapid prototypingmachine (GoEngineer; Santa Clara, CA) (Fig. 2(b)) and wasdesigned to meet the requirements set by the American Societyof Mechanical Engineers (ASME) [25].

With these requirements, the flow meter theoreticallyoperates within a Reynolds number range of 104–107

(turbulent regime). For the physical size dimension in our flowchamber, we find that flow rates from 300 L/min to 1000 L/minhave Reynolds numbers on the order of 104.

3) Selection of Sensors: PEF values vary depending ongender, age, and height of an individual [26]. In healthyadult women, the maximum flow rate is approximately450–500 L/min while in healthy adult men, the maximum flow

rate observed is approximately 600–650 L/min [26]. Low flowrate in this study was considered to be exhaled flow rate thatis at or below tidal breathing, which has been reported to bearound 41 ± 11 L/min in healthy adults [27].

Therefore, to fully capture the dynamic range of exhaledbreath, two piezoresistive pressure sensors were selected tomonitor high flows of 50–900 L/min (pressure sensor A;model #MPX5010; Freescale Semiconductor; San Jose, CA),and low flows of 15–100 L/min (pressure sensor B; model#SSCSNBN002NDAA5; Honeywell; Morristown, NJ).

The dynamic ranges for three of the chemicalbiomarkers found in exhaled breath in asthma patients are0.02–0.13 parts per million (ppm) for NO [28-31], 2–7 ppmfor CO [32, 33], and 14–20 parts per hundred (pph) forO2 [34] whereas in healthy individuals the dynamic ranges are0.005–0.02 ppm for NO, 1–2.3 ppm for CO, and 14–20 pphfor O2 [31, 34]. The chemical sensors were selected todetect the lower end of the biomarker concentration rangefound in exhaled breath in asthma patients (model numbersNO-D4, CO-D4, and O2-G2; AlphaSense Ltd.; Essex,United Kingdom).

These three sensors are electrochemical sensors. Theoxygen sensor has a slight humidity dependence while theNO and CO sensors do not have a humidity dependencebut have signal spikes from rapid transient changes inhumidity [35, 36]. The NO and CO sensors are rated for 80%of the original signal after 2 years while the oxygen sensoris rated for 85% of the original signal after 2 years [37-39].

For the NO and CO sensors, a potentiostatic circuit was builtto control the chemical sensor and a transimpedence amplifierwas used to convert the current generated from sensors toa measureable voltage. The O2 sensor does not require apotentiostatic circuit and the signal was obtained by using atransimpedence amplifier to convert the current generated bythe sensor into a measureable voltage.

Quantification of chemical biomarkers in exhaled breathmust also occur before spirometry maneuvers becausespirometry often causes exhaled NO concentrations toartificially decrease [40].

4) Microcontroller and USB Host Shield: A microcontroller(Arduino UNO, R2; Strambino, Italy) was paired with aUSB host shield (Sparkfun, DEV-09947; Boulder, CO) tocontrol the external sensors and transmit data back to thesmart device. The Arduino UNO and USB host shield systemsends the digital signal in a three byte message from themicrocontroller to the Android mobile device using a USBconnection. During the first breath maneuver, the microcon-troller collects data from the three chemical sensors first ata rate of 100 samples/s for a total of 15s (data from eachchemical sensor is recorded for five seconds in a sequentialmanner). In a second breath maneuver for spirometry testing,data points were collected from pressure sensors A and B at arate of 50 samples/s. The microcontroller alternates samplingbetween each pressure sensor which occurs for a total of 18 s,allowing ample time for the patient to perform the spirometrymaneuver.

5) Software Application Development: An Androidsoftware application for Android devices with the Gingerbread


Fig. 3. Flowchart of the Android software application program.

platform (version 2.3.3) was created to extract important datavalues from the microcontroller and to provide an interfacefor the user (Fig. 3). Development was completed usinga Motorola Xoom tablet. The application was developedin the Eclipse Indigo Integrated Development Environment(version 3.7.1) and written in Java programming language(version 1.6).

Each data point received from the microcontroller isinterpreted using linear regression equations determined fromthe subsequent calibration experiments. All sensor data, time,date, and global positioning system (GPS) location are writtenin a comma separated value (.csv) file. Only the averageNO, CO, and O2 concentrations, PEF, instantaneous flowrate, and the spirometry graph are shown to the patient on theAndroid tablet screen (Fig. 3). These data can be e-mailedusing the native operating system e-mail application on thetablet.

The software application prompts the patient to perform70 seconds of tidal breathing to collect exhaled biomarker data.Patients have five seconds of rest before the device signalsthe patient to perform a full spirometry breath maneuver(exhalation for at least 6 seconds to obtain a suitable FVCalternate and an acceptable spirometry maneuver). PEF valueswere identified from the spirometry maneuver.

To create a spirometry graph, flow rate measurementsfrom each pressure sensor are recorded in separate arrays.After the maneuver has been completed, data from bothpressure sensors are merged. Data from pressure sensor Bare recorded if the flow rates from pressure sensor A areless than 100 L/min. Otherwise, data from pressure sensor Aare recorded. The start of the test is determined using theback extrapolation method recommended by Miller, et al. [9]and the new “time-zero” denotes the start time for all timedmeasurements. The volume flow rate time-series is integratednumerically using a second-order method to produce acorresponding exhaled-volume time series needed to plot thespirometry loop and evaluate FEV1 and FEV6.

Data obtained by each chemical sensor received from themicrocontroller is converted to a concentration value whenreceived by the software application. A numerical array wascreated for each chemical biomarker and all data received afterthe fifth time constant found for each sensor was averaged toobtain an estimate of the concentration of the three chemicalbiomarkers in exhaled breath.

B. Calibration and Validation of HardwareSystem Performance

1) Pressure Sensor Calibration: Compressed air anda digital mass flow controller (Omega FMA 5545-ST;Stamford, CT), were used for calibration. The voltageoutput signal response from pressure sensor A was recordedfor flow rates ranging from 0–500 L/min in 50 L/minincrements (n = 5). For pressure sensor B, the same setupwas used but was calibrated from 0–100 L/min in 10 L/minincrements (n = 5).

2) Chemical Sensors Calibration: Three tanks ofcompressed gas (1 ppm NO, 10 ppm CO, and 15 pph O2)were purchased from Airgas, Inc. (Sacramento, CA) anddiluted to concentrations found in asthma patients using gasproportioners. The calibration was performed five times foreach chemical sensor at each concentration. Specifically, theconcentrations of NO gas used to calibrate the NO sensorwere: 0, 0.033 ppm, 0.05 ppm, 0.067 ppm, 0.1 ppm,0.125 ppm, 0.2 ppm, 0.33 ppm, 0.5 ppm, and 1 ppm. Theconcentrations of CO gas used to calibrate the CO sensor


Fig. 4. (a) Pressure sensor A calibration. (b) Pressure sensor B calibration.

were: 0 ppm, 1 ppm, 1.67 ppm, 2 ppm, 2.5 ppm, 3.33 ppm,5 ppm, 7 ppm, and 10 ppm. The concentrations of O2 gas usedto calibrate the O2 sensor were: 3 pph, 3.3 pph, 3.7 pph, 5 pph,7.5 pph, 8.8 pph, 10 pph, 12.5 pph, 15 pph, and 20.9 pph.

3) Estimation of the Chemical Sensor Noise Floor: Thenoise floor of each sensor was calculated according toEquation 3,

NoiseFloor = x̄ + 3σ (3)

where x̄ represents the mean of the signals collected in ambientair and σ is the standard deviation. The intersection betweenthe linear regression line and the noise floor is marked as thelimit of detection (LOD). The LOD indicated for each sensorsignifies the minimum concentration of analyte distinguishablefrom the background noise of the sensor.

4) Time Constant Determination: Each chemical sensor wasspecified by the vendor to have a response time to 90% ofthe final concentration of less than 25 seconds. Determinationof the time constant identified the requisite time for tidalbreathing. At five times the time constant, the sensors willreach 99.3% of the final concentration. The change in chemicalconcentration from ambient conditions to the breath sampleproduces a step response which can be characterized for afirst-order system by equation 4.

y = 1 − exp(1

τt) (4)

where y is the percentage of the final value, t is the time inseconds since the step, and τ is the time constant. Given thatthe time to 90% of the final value is known, equation 4 canbe rearranged to solve for the time constant.

Fig. 5. (a) Nitric oxide sensor calibration. (b) Carbon monoxide sensorcalibration. (c) Oxygen sensor calibration.

III. RESULTS

A. Calibration of Pressure SensorsBoth pressure sensors were calibrated to correlate sensor

output with flow rate through the device. The flow equation(Eq. 1) shows that the pressure drop across the obstructionflow meter is proportional to the square of the flow rate. Thecorrelation equations needed to convert the voltage output, V,of each pressure sensor into a flow rate value Q (L/min) wereconstructed accordingly: V = αk Q2 + βk (with k = A, B).Linear regression yielded the values of the parameters for eachsensor: αA = 7 × 10−6 V/(L/min)2, βA = 1.83 × 10−2 V(R2 = 0.996) and αB = 1.11 × 10−4 V/(L/min)2,βB = 2.567 V (R2 = 0.989) as shown in Fig. 4(a) and (b).These correlation equations were coded into the softwareapplication. With calibration, pressure sensor A is ableto accurately measure flow rates as low as 50 L/min(14.4% error). Pressure sensor B enables the measurement offlow rates as low as 15 L/min with a 5.517% error.

B. Chemical Sensors and Time Constants

All three chemical sensors exhibited a linear relationship(V = αk C + βk with k = NO, CO, O2) between analyteconcentration, C, and sensor output voltage, V (Fig. 5(a)-(c)).


They were calibrated to establish the values of theregression parameters for each sensor: αNO = 0.514V/ppm,βNO = 0.176V (R2 = 0.999); αCO = 0.325V/ppm,βCO = 0.732V (R2 = 0.999); αO2 = 0.141V/pph,βO2 = 0.660V (R2 = 0.995) for NO, CO, and O2 respectively.

The limits of detection are (Fig. 5(a)-(c)): LODNO =0.029 ppm (0.190 V noise floor), LODCO = 0.945 ppm(0.1038 V noise floor), and LODO2 = 4.722 pph (1.308 Vnoise floor). The NO LOD is very close to the low-end valuesof NO concentrations expected for asthma patients (0.03 ppm).This indicates that while the NO sensor is theoretically suitableto measure NO in exhaled breath at such low concentrations,the output values may not be reliable for concentrations ator lower than 0.03 ppm. The CO LOD and O2 LOD arerespectively lower than the lowest CO concentrations expectedin the breath of asthma patients (2 ppm) and well below thelowest O2 concentration in breath (14 pph).

The chemical sensor response times are provided as t90 intheir data sheets which are <15, <20, and <25 seconds forNO, O2, and CO respectively. Using equation 4, this results intime constants of 6.51, 8.69, and 10.86 seconds for NO, O2,and CO respectively. Thus, the patient needs to breath for atleast 55 seconds in order for the sensors to reach their finalvalue.

C. Chemical Sensor Performance Using Gas Mixtures

The performance of each chemical sensor was verified toconfirm their ability to measure the concentration of a targetgas in mixture with other two gases of interest (e.g. NO alongwith CO, and O2) with acceptable precision and accuracy.Specific concentrations were achieved by controlling the flowrate ratios of each gas. The three gases were mixed usingtwo gas proportioners to produce three distinct mixtures thatcontained concentrations of NO, CO, O2 within the rangefound in the exhaled breath of asthma patients. A standardprocedure was used to prepare each mixture. Three tanks ofspecialty gas were used for this experiment (1 ppm NO tank,1000 ppm CO tank, and 15 pph O2 tank). NO and O2 werefirst mixed in a gas proportioner with a flow rate ratio of1 NO:X O2. This NO/O2 mixture was then sent to anothergas proportioner where it was mixed with CO. The flow rateratio for the second gas proportioner was 1 CO:Y NO/O2.

Because the concentration of exhaled O2 should be similarfor all individuals, all gas mixtures were made with an O2 con-centration of 13–14%. This oxygen concentration was chosento reflect the low end of the sensor range in order to confirmthat the sensors could operate the lowest O2 concentration inhuman breath.

For NO and CO, the concentrations were varied betweenthe upper and lower limits of the concentrations in theexhaled breath of asthma patients. The first mixture containedNO and CO concentrations at the upper limit of the concen-tration range of those with asthma and O2 at the low endof the concentration range in human breath (0.099 ppm NO,6.944 ppm CO, and 13.406 pph O2; X = 9 and Y = 143).

The second mixture contained NO and CO concentrationsnear the middle of the concentration range of those with

TABLE I

CHEMICAL SENSOR RESPONSE TO GAS MIXTURES OF

NITRIC OXIDE, CARBON MONOXIDE, OXYGEN

asthma and O2 at the low end of the concentration rangein human breath (0.066 ppm NO, 4.975 ppm CO, and13.930 pph O2; X = 14 and Y = 200). The third mixturecontained NO and CO concentrations at the low end ofthe concentration range of those with asthma and O2 atthe low end of the concentration range in human breath(0.033 ppm NO, 1.996 ppm CO, and 14.471 pph O2; X = 29and Y = 500).

Each gas sensor was exposed to the three gas mixturesfive times and the resulting concentration readings wererecorded (Table I). A two-tailed t-test was used to compare themeasured and theoretical gas concentrations. For the mixtures,only oxygen was significantly different, which indicates thatthe oxygen sensor likely needs to be recalibrated.

D. Spirometry Accuracy and Repeatability

Prior to performing a prospective validation study withasthmatic and non-asthmatic subjects, we believed it wascritical to test the performance and accuracy of this asthmamonitoring device in the laboratory setting. Performancewas assessed and compared against a spirometer currentlyapproved for use in a clinical setting (SDI Diagnostics,SPIROLABII).

A group of 10 subjects was asked to perform threeacceptable spirometry maneuvers using the clinicalspirometer under the supervision of a health care professional.An acceptable maneuver is described as a full, uninterruptedexhalation of breath that was not interrupted (coughing,hesitation, etc.), had satisfactory exhalation duration (≥6 s),and resulted from maximum exhalation effort by the patient.The same subjects were then asked to perform three moreacceptable spirometry maneuvers using the asthma monitoringdevice. Only forced exhalation is assessed with the asthmamonitoring device, thus only PEF, FEV1, and FEV6.


Fig. 6. (a) Ideal inhalation and exhalation flow-volume spirometry graph froma theoretical normal healthy adult. (b) Exhalation flow-volume spirometrygraph generated by a subject using the asthma monitoring device.

Repeatability of the asthma monitoring device wasevaluated in accordance with the requirements established bythe American Thoracic Society and the European RespiratorySociety [9], where the two largest values of FEV1 and FEV6must be within 0.150 L. The 10 test subjects were able toperform three acceptable spirometry maneuvers on the clinicalspirometer before performing three acceptable spirometrymaneuvers on the asthma monitoring device without difficulty.During the spirometry maneuver, the flow rate increases ata steep positive slope until the peak flow is reached,approximately one second from the start of exhalation. Theflow rate then gradually decreases until the total lung volumehas been exhaled, approximately 6 seconds from the start ofexhalation. The volume of exhaled breath is plotted on thex-axis of the spirometry graph. At the end of the 6 seconds ofexhalation, the flow rate reaches zero and the correspondingx-axis value would be considered the total lung capacity forthat subject (Fig. 6(a) and 6(b)). For each subject, the averages

TABLE II

COMPARISON OF THE ASTHMA MONITORING DEVICE

WITH A CLINICAL SPIROMETER

of the three values of PEF, FEV1, and FEV6 recorded bythe asthma monitoring device were compared against theaverages of the same parameters taken from the clinicalspirometer using 2-block analysis of variance (ANOVA)with one block covering the variation amongst people andthe other covering the variation between the devices. Thecollected data is summarized in Table II and Fig. 7(a)-(c).

While there was no significant difference between thedevices for PEF and FEV1 measurements (F(1,9) = 2.381,


Fig. 7. (a) Average PEF values from all subjects using the asthma monitoringdevice and a clinical spirometer. (b) Average FEV1 values from all subjectsusing the asthma monitoring device and a clinical spirometer. (c) AverageFEV6 values from all subjects using the asthma monitoring device and aclinical spirometer.

p = 0.157 and F(1,9) = 0.653, p = 0.440 respectively),there was a significant difference in the FEV6 measurements(F(1,9) = 38.541, p = 0.00016).

IV. DISCUSSION

This study presents a new approach to monitoring lungfunction in asthma patients with a novel portable devicethat operates using a smart phone or tablet. This deviceallows the acquisition of the expiration branch of a spirometrytest and the associated parameters (i.e. PEF, FEV1, FEV6),as well as the quantification of relevant exhaled biomarkers(NO, CO). Initial testing of this asthma monitoring devicein the laboratory setting has demonstrated its capability tomeasuring major lung function parameters with reasonableaccuracy and precision.

The flow metering function of the device is performedusing an obstruction flow meter equipped with two differential

pressure sensors, each focusing on half of the expected volumeflow rate range. High flow rates are generated at the beginningof the spirometry maneuver and pressure sensor A was neededto ensure that the device captured PEF values accurately. Therewas no significant difference between the PEF value measuredby a clinical spirometer and that obtained with the asthmamonitoring device. Hence, pressure sensor A effectively mea-sures PEF. Pressure sensor B was selected to capture the lowflow rates (<50 L/min) that are characteristic at the end of aspirometry maneuver. While pressure sensor B does measureflow rates lower than tidal breathing effectively, it cannotaccurately measure flow rates that are below 15 L/min. Thislimitation may result in an underestimation of FEV6 as thedevice does not quite capture the full spirometry maneuver.Accuracy in the low flow rate range could be improved withoutchanging the overall design concept by implementing a moresensitive and higher cost, pressure sensor.

A ten-sample test was conducted in the research settingto determine if the asthma monitoring device can performa spirometry test comparable to the expiration branch of aclinical spirometer. As noted above, the PEF values acquiredusing the asthma monitoring device are not significantlydifferent from the clinical spirometer. After several forcedexpiratory maneuvers, patients may experience fatigue andbe inclined to stop exhalation before they have completedthe maneuver. Spirometry measurements are still consideredacceptable if the drop in FEV1 or FEV6 does not exceed20% if more than three maneuvers are required [9, 41] whichis the case for all measurements of the 10 subjects. Furthertesting is needed to determine if the asthma monitoring devicecan accurately detect lung function parameters in those whoregularly smoke tobacco. The significant differences in FEV6values were expected because of the limitations in pressuresensor B, which does not allow the asthma monitoringdevice to accurately detect flow rates lower than 15 L/min.Furthermore FEV6 values measured are often dependent onthe effort the patient puts into fully completing the spirometrymaneuver, which can be inconsistent.

The variability in measured spirometry lung functionparameters from the asthma monitoring device and the clinicalspirometer were observed to be very similar. For PEF values,the standard deviation of measurements taken on the asthmamonitoring device ranged between 5.119–56.102 L/min,while the clinical spirometer had a standard deviationrange of 5.574–83.302 L/min. The standard deviation ofFEV1 measurements taken on the asthma monitoring devicewas 0.023–0.333 L, and the clinical spirometer had a standarddeviation of FEV1 measurements of 0.015–0.405 L. The stan-dard deviation of FEV6 measurements from the asthma mon-itoring device was between 0.071–0.373 L, and the clinicalspirometer had standard deviations of FEV6 values between0.010–0.260 L. Aside from illustrating the inherent variabilityof spirometry measurements, this data also shows that 9 out ofthe 10 subjects had their PEF and FEV1 measurements fromboth devices overlap with one another. This along with the lackof a significant difference indicates that the asthma monitoringdevice can measure these parameters with an accuracy andprecision comparable to that of the clinical spirometer.


The chemical sensors used in this work (AlphaSense, Ltd.)were selected for their combination of high sensitivity, highselectivity, miniature size, and short response time comparedto other available chemical sensors. They were shown to havea linear relationship between voltage and concentration ofanalyte and the CO and NO chemical sensors were able todetect various concentrations of their target analytes accuratelywithin a mixture (no significant difference). The differencein the oxygen sensor is likely due to a miscalibration whichcan easily be corrected. The time constants determined for thechemical sensors ranged from 6.51 seconds for the NO sensorto 10.86 seconds for the CO sensor. Patients therefore need toperform tidal breathing, exhaling through their mouth, for atleast 70 seconds (55 for the sensors and 15 to read the values)to ensure that the chemical biomarkers in their breath can beread. This duration might be difficult for severely asthmaticpatients and future design improvements should incorporatechemical sensors with a shorter response time and better sen-sitivity to the target analyte. From experimentation it was seenthat the NO LOD (0.029 ppm NO) was very close to the lowerend of the dynamic range found in asthma patients (0.03 ppm).This would normally indicate that at concentrations as low as0.03 ppm NO, the sensor output signal could be confused withthe background noise of the sensor. However, as seen in theexperiment using three gas mixtures, the NO sensor was ableto detect a 0.03 ppm NO concentration with 3.727% error.Although the lower limit of 0.03 ppm is above the lowerbound of the American Thoracic Society (ATS) guidelines(0.025 ppm for adults and 0.02 ppm for children), it is belowthe lower bound of the group that is likely to benefit frommedication (>0.05 ppm for adults and >0.35 ppb for children)[31]. This range would indicate that this device would be ofparticular benefit to those beginning medication.

A key feature of the asthma monitoring device describedhere is its ability to measure multiple lung function parametersthrough two breath maneuvers. Though there is a number ofpersonal PEF, spirometers, and NO sensing devices currentlyon the market, a device that combines PEF measurements,spirometry, and NO, CO, and O2 biomarker detection isnew. One of the advantages of being able to measure PEF,spirometry, and exhaled chemical biomarkers at once on asmart phone or tablet is the opportunity to utilize the built-inconnectivity features such as GPS and e-mail. The abilityto gather the necessary data quickly and efficiently and theninstantly communicate that data with a health care professionalmeans that such devices have the potential to significantlyimprove the speed of respiratory health care and asthmamanagement in the future.

Given the ubiquity of advanced mobile devices today,designing personal medical monitors to be compatible withsmart phones and tablets is an effective way to help lowerdevelopment costs and increase user compliance. An integralpart of ensuring that patients have the tools to control andmanage their own asthma symptoms is to provide patientswith an accurate, reliable, and portable device that can beaccessed at any time. A major problem associated with currenthandheld PEF devices available to patients is that valuesmust be written and recorded in a daily symptoms journal.

The goal of the portable nature of our asthma monitoringdevice and its ability to connect with Android mobile devicesis to encourage asthma patients to play a more active rolein controlling their disease. Another important advantage ofour device is its capability of saving each test onto theAndroid tablet and e-mailing the recorded data, which giveshealth care professionals a chance to study the symptomsand lung function patterns of a patient over time. Thesepatterns may shed light on trends regarding asthma triggersand the effectiveness of new medications and treatments. Theasthma monitoring device detailed in this paper representsa simple solution to improve asthma monitoring complianceby providing a portable, inexpensive, and user-friendly optionthat gives both patients and doctors a complete snapshot oflung health.

V. CONCLUSION

We have designed, built, and validated a personalized lungfunction monitoring device that utilizes smart phone and tablettechnology to create a convenient, reliable, and user-friendlysystem. Initial validation testing has proved that measurementstaken with this device are comparable to that of a clinicalspirometer and satisfy the minimum requirements for spirom-etry as outlined by Miller and colleagues. Advancementstoward personalized medicine provide more opportunities toperform longitudinal studies with asthma patients remotelyand enable patients to become more aware of their lung health.

ACKNOWLEDGMENTS

The authors would also like to thank Dr. M. Delwiche(Biological and Agricultural Engineering; University ofCalifornia, Davis) for instrumentation advice, and ScanaduInc. for interest in the project.

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Alice M. Kwan received the B.S. degree in biomedical engineering from theUniversity of California at San Diego, La Jolla, CA, in 2008 and the M.S.degree in mechanical engineering from the University of California at Davis,Davis, CA, USA, in 2012. She is currently working in the medical deviceindustry.

Alexander G. Fung is currently pursuing the Ph.D. degree in mechanicalengineering at the University of California at Davis, Davis, CA, USA, wherehe received the B.S. degree in mechanical engineering, in 2011.

Peter A. Jansen is currently a Post-Doctoral Research Fellow with theUniversity of Arizona, Tucson, AZ, USA. He was with Scanadu, Inc., asa Senior AI Engineer and received the Ph.D. degree in neural computationand cognitive language from McMaster University, Hamilton, ON, Canada,in 2010.

Michael Schivo is currently with the Reversible Obstructive Airway DiseasesCenter, the UC Asthma Network Clinics, and the Center for ComparativeRespiratory Biology and Medicine, University of California at Davis, Davis,CA, USA. He also has a general pulmonology clinic once a week in Rocklin,CA, USA. He collaborates highly with biologists, chemists, chemometricians,engineers, and pediatric/adult clinicians to coordinate clinical trials utilizingVOC analysis in disease states. His research spans from in vitro epithelialmodels to human subject studies.

Nicholas J. Kenyon is currently a Professor with the University of Californiaat Davis (UC Davis), Davis, CA, USA. His translational research programfocuses on severe asthma and COPD, and the role of nitric oxide in airwayinflammation. He joined the faculty of UC Davis in 2001.

Jean-Pierre Delplanque is currently a Professor with the University ofCalifornia at Davis (UC Davis), Davis, CA, USA, where his research programfocuses on theoretical and computational fluid dynamics and transport phe-nomena. His specific applications include inert and reactive multiphase flowsystems, and emerging materials processing methodologies and processes.He has been with UC Davis since 2004.

Cristina E. Davis is currently a Professor with the University of Californiaat Davis (UC Davis), Davis, CA, USA, where her research program focuseson design and implementation of sensors and instrumentation for biologicalapplications. She has been with UC Davis since 2005.

06965590 personal lung function

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