EXHIBIT 2001
`
`EXHIBIT 2001
`
`

`

`Earbud-Based Sensor for the As_sessment
`of Energy Expenditure, HR, and V02“,x
`
`STEVEN FRANCIS LEBOEUF', MICHAEL E. AUMER', WILLIAM E. KRAUSZ, JOHANNA L. JOHNSONZ,
`and BRIAN DUSCHAZ
`
`I Valencell, 1nc., Raleigh, NC; and 2Division ofCardiology, Department ofMedicine, Duke University
`School ofMedicine, Durham, NC
`
`ABSTRACT
`
`LEBOEUF, S. F., M. E. AUMER, W. E. KRAUS, J. L. JOHNSON, and B. DUSCHA Farbud-Based Sensor for the Assessment of
`Energy Expenditure, HR, and V02“. Med. Sci Spars Exem, Vol. 46, No. 5, pp. 1046 1052, 2014. Introduction/Purpose: The goal
`of this program was to determine the feasibility of a novd noninvasive, highly miniaturized optomeclnnical earbud sensor for accurately
`estinnting total energy expenditure (TEE) and maximum oxygen consumptim 0702...“). The optomechanical sensor module, small
`enough to fit inside commercial audio earbuds, wm previously developed to provide a seamless way to measure blood flow infomra—
`tion during daily life activities. The sensor module was configured to continuously measure physiological information via photo-
`plethysmography and physical activity infomratim via accelerometry. This information was digitized and smt to a microprocemr
`where digital signal—processing algorithms extract physiological metrics in real time. These metrics were streamed wirelessly floor
`the carbud to a computer. Methods: In this study, 23 subjects of multiple physical habitus were divided into a training group of 14
`subjects and a validation group of 9 subjects. Each subject underwent the saute exerdse measurement protocol consisting of treadmill—
`based cardiopulmonary exercise testing to reach V0“. Benchmark sensors included a 12-lead F136 sensor for measuring HR, a
`calibrated treadmill for measuring distance and speed, and a gasexchange analysis instrument for measuring TEE and V0“. The
`carbud sensor was the device under test. Benchmrk aid device under test data collected hour the 14—person training data set study
`were integrated into a preconceived statistical model for correlating benchmark data with earbud sensor data. Coetficients were op—
`timized, and the optimized model was validated in the 9—person validation data set. Results: It was observed that the carbud sensor
`estimated TEE and V02...“ with mean 1 SD percent estimation errors of 0.7 L 7.4% and
`3.2 1 7.3%, respectively. Conclusion: The
`carbud serrsorcan accurately estimate TEE and V02...“ during cardiopuhnomry exercise testing. Key Words: FAR, ACCIlEROMEl'HI,
`PHOTOPLETHYSMOGRAPHY, PULSE
`
`odifiable health risk factors, such as high stress,
`poor diet, and sedentary lifestyle, account for 25%
`of all medical expenses and nrillions of deaths
`per year worldwide (2). The US. population is becoming
`increasingly overweight and unhealthy, with an estimated
`66% of adults categorized as obese or overweight by the
`CDC (26). Nonetheless, more than half of American adults
`exercise on a regular basis (1 l), spending more than $55
`billion in weight loss programs and more than $17 billion
`on fitness products (31). The disconnect between dollars
`spent on weight loss and obesity levels may be explained by
`recent findings that traditional diets do not work (24) alone
`
`Addras fir carespondence: Steven Francis LeBoeuf, PhD, Valencell, lnc.,
`28(0154 Sumner Blvd, Raleigh, NC 27616; E—nrail: IeBocu@valencell.com
`Submitted for publication March 2013.
`Accepted for publication October 2013.
`0195-9131/14/4605-1046/0
`MEDICINE & SCIENCE IN SPORTS & EXERCISE,
`Copyright© 2014 by the American College of Sports Medicine
`D01: 10.1249/MSS.(X)00000000000183
`
`to prevent weight gain and to promote weight loss. Dietary
`measure must be combined with energy expenditure to ac-
`complish long-term weight loss and maintenance.
`Weight loss programs aimed at promoting fitness through
`direct measurement of physical activity (PA) via pedometer
`feedback have shown promise. In particular, incorporating
`a pedometer in daily life activities has been shown to result
`in a significant reduction in body mass index (BMI) and
`blood pressure (7). Furthermore, combining engaging feed-
`back with an online user experience correlates with im-
`proved maintenance of weight loss in long-term diet/weight
`management studies (10). These observations indicate that
`even better outcomes may result from a more direct feed-
`back about energy expenditure and aerobic fitness level,
`such as V02, calories burned, and VOZM.
`Indeed, there is a clear opportunity to encourage a broader
`population to embrace active lifestyles by integrating mobile
`fitness monitoring devices with compelling user experiences.
`However, compelling user experiences must be meaningfirl,
`and to be mcaningfirl, the fitness monitoring gadgets must
`provide information that is sufficiently accurate to be action-
`able. This goal is challenged by the fact that commercial pe-
`dorneters are inaccurate by greater than i 20% in reporting
`calories buried (8,29).
`
`1046
`
`Copyrigth 2014 by the American College of Sports Medicine. Unaulhorized reproduction of this article is prohibited.
`
`VALENCELL_0001
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`FIGURE 1 The components and size of the device under test (DUT).
`Shown are the earbud and the medallion containing the majority of
`the computational components. Shown for scale is a U.S. quarter. Note
`the position of the sensor module at the bottom of the antitragus. The
`sensor module is configured to fit between the concha and the antitragus
`ot' the ear.
`
`Recent enagy expenditure studies, using a wearable Acti-
`Health chest strap monitor for measuring both PA and HR,
`have demonstrated greater accuracy (5,6). These researchers
`achieved such predictive accuracy through branched equation
`modeling, using HR information and accelerornetry infor-
`mation as independent variables. Although these findings are
`quite encouraging, researchers using the ActiHealth monitor
`point out several shortcomings. First, despite the relatively
`high precision achievable through branched equation model-
`ing, poorer accuracy is observed if individual calibratiom are
`not used (5,6). This means that the wearable monitors must
`be calibrated for each user, in a process that is both time con-
`suming and burdemome. Furthermore, as audio earbuds are
`packaged with smartphones and digital media players that
`are sold in volumes of hundreds of millions of units a year
`(16), the audio earbud form factor provides the opportunity
`to reach a larger consumer audience than that of an HR
`chest strap, which is sold in volumes of less than 10 million
`per year.
`The goal of this study was to determine the feasibility of
`a highly miniaturized, noninvasive optomechanical earbud
`sensor technology for accurately monitoring physiological
`metrics such as HR, total energy expenditure (TEE), and
`
`maximum oxygen consumption (V02",“), and this study is
`reported herein.
`
`METHODS
`
`To overcome these reported limitations, an earbud sensor
`module—as opposed to an ActiGraph wrist-, ann-, or leg-
`wom sensor—was selected in this study (Fig. 1). Details
`of the mechanism of operation are described elsewhere
`(17—21), but in summary, the earbud comprised a highly
`integrated sensor module capable of measuring subtle blood
`flow changes via reflective photoplethysrnography (PPG)
`and changes in body motion through a three-axis acceler-
`ometer. This sensor module was designed l) to capture and
`digitize the optical PPG signal and 2) to send the digitized
`information to a digital signal processor (DSP) for remov-
`ing motion artifacts and environmental noise fi'om the PPG
`signal and to continuously generate estimates of HR and
`V02 metrics in real time based on a statistical model com-
`prising PPG and accelerometry information. The DSP was
`in electrical communication with a Bluetooth chipset so that
`the real-time metrics could be called upon by a client de-
`vice (such as a laptop or smartphone). A preliminary feasi-
`bility study of this PerfonnTek® earbud sensor module had
`previously demonstrated accurate HR measurements during
`exercise, thus potentially eliminating the need for an elec-
`trocardiographic chest strap in many use cases. This was a
`critical finding for the issue of user compliance, as 58% of
`US. headphone owners listen to headphones while exercis-
`ing and 34% wear headphones during everyday life activities
`(such as doing work around the house) (13), 10 times greata
`than the number ofAmericans who exercise with chest straps.
`Subjects. In this study, 23 subjects of good physical
`health were divided into a training group of 14 subjects and
`a validation group of 9 subjects. This sample size is justified
`by the high “effect size” observed for ealibrated correla-
`tions of V02 and HR (22) and is further supported by the
`very high R2 coefficient observed (23) when comparing
`the earbud-detennined HR to 12-lead ECG-measured HR
`
`during exercise. The training group (Table la) comprised
`12 men and 2 women: age = 39 i 11.8 yr, weight = 73.5 i
`12.2 kg, height = 69 i 2.9 cm, BMI = 23.6 i 2.1 kg-m‘z.
`The validation group (Table lb) comprised five men and
`four women: age = 36 i 6.9 yr, weight = 67.6 i 15.7 kg,
`height = 173 i 7.4 cm, BMI = 22.3 r 4.0 kg-m'z. Each
`
`TABLE 1. Descriptive characteristis (mean + SD) of (a) trailing group and (b) validation group.
`(a) Training Group
`
`(Ir) Validation Group
`
`Paramalr
`sex
`Age
`Weight
`Height
`Distance
`Energy expeudiirre
`Maximum V02
`MI
`
`Value (Moan + SD)
`2 females. 12 males
`30 r 11.8 yr
`73.5 + 12.2 kg
`175 + 7.4 cm
`2.95 + 0.5 km
`213 + 47.8 keel
`55.9 + 6.5 mL-kg 1min ‘
`23.6 + 2.1 kgm 2
`
`Paramahr
`Sex
`Age
`Weight
`Height
`Distance
`Energy amenditure
`Maximum V0;
`BMI
`
`Value (than + SD)
`4 females. 5 males
`36 + 59 yr
`67.6 + 15.7 kg
`173 . 7.4 cm
`230 + 0.3 km
`178 + 51.5 keel
`5.1 r 5.5 mL‘kg 1emin '
`23 + 4.0 kgAm 2
`
`EAFBUD—BASED 834801? FOR PHYSICAL ASSESSMENTS
`
`Medic'ne & Science in Sports & Exercise,
`
`1047
`
`Copyrigth 2014 by the American College of Sports Medicine. Unauthorized reproduction of this article is prohibited.
`
`VALENCELL_0002
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`subject underwent the same exercise measurement proto—
`col, including a treadmill-based cardiopulmonary exercise
`(CPX) test, at 0° incline, to reach (102...... The achievement
`of ‘70me was determined by reaching at least two of the
`three following criteria: plateau in V02 over the last minute
`of exercise, achievement of at least 1.10 RER, arnd achieve—
`ment ofat least 17 in perceived exertion on the Borg scale. The
`mean i SD V02“, values of the training goup arnd the vali-
`dation goup wee 55.9 i 6.5 arnd 55.1 i 5.5 mL-kg_'-min_',
`respectively. Benchmark sensors included a 12-lead ECG for
`measuring HR, a calibrated treadmill for measuring distance
`traveled, and a gas-exchange analysis instrument for mea-
`suring TEE and VOme. The earbud sensor served as the
`device nmder test. All subjects provided informed consent
`as approved by the investigatimal review board of the Duke
`University School of Medicine.
`CPX testing. Subjects began the study by first being
`prepped for wearing the benchmark sensors. A Quinton12-
`lead ECG system was used as a bernchrnark for HR, and a
`TmeMax 2400 ParvoMedics (ParvoMedics, Sandy, UT) gas-
`exchange analysis mouthpiece was used as a benchmark for
`energy expenditure and continuous measures of V02. The
`benchmark sensors were calibrated according to the stan-
`dard maintenance guidelines of the manufacturers. The sub-
`jects were then fitted with an earbud sensor (Fig. 1) powered
`by the aforementioned PerformTek physiological monitor-
`ing technology. Participants were then asked to sit at rest in
`a supine position in a reclining chair for a few minutes while
`wearing the benchmark equipment and earbud sensor. Afte-
`the resting period, subjects were instructed to move from the
`chair to the calibrated treadmill and execute the CPX testing
`with graded irntensity ranging from 0 to 9.1 mph speeds. The
`protocol used comisted of 2-min stages, increasing the work-
`load by approximately one metabolic equivalent per stage.
`Measurements from the benchmark sensors arnd earbud sen-
`
`sor were collected continuously throughout the treadmill mn.
`Participants were asked to corntinue mnning during each in-
`creasing speed until they were completely exhausted. The
`lxt 40 s of benchmark gas-exchange analysis data were av-
`eaged to determine measured peak V02.
`Earbud sensor. The novel noninvasive earbud sen-
`
`sor (Fig. 1) used in this study was designed by Valencell,
`Inc. (Raleigh, NC). The earbud sensor comprised a sensor
`module, a microprocessor, arnd a wireless Bluetooth® chipset.
`The optomechanical sensor module, comprising the sensor
`elements, was embedded within the right audio earbud, as
`shown in Figure 1, such that the sensor module would rest
`between the concha and the antitragus of ench subject upon
`earbud placement. The right arnd the lefi earbuds were de-
`signed to be pluggable to a wireless Bluetooth “medallion”
`via a detachable connector (as shown in Fig. l). The medal-
`lion housed the microprocessor arnd the Bluetooth chipset.
`At tlne heart of this noninvasive earbud sensor is a highly
`miniaturized optomechanical module (17—21,23) that mea-
`sures optieal and mechanical information from the area of a
`user’s ear between the antitragus and the concha. This novel
`
`sensor module comprises an infrared light-emitting diode,
`a photodetector element, a three-axis accelerometer, and an
`optonnechanical housing. Designed to fit flush with the body
`of a standard audio earbud, the earbud essentially maintains
`the form factor of a typiml audio earbud and does not re-
`quire an ear clip or an in—ear-canal sensor to firnction.
`The optical arnd mechanical information collected fiom
`the car are sampled via methods akin to reflective PPG and
`three-axis accelerometry, and this sampled information is
`then processed by novel algorithms (17,18) coded on finn-
`ware within the microprocessor for extracting weak blood
`flow signals from excessive motion noise. It is well known
`that motion artifacts are the greatest limiting factor to accu-
`rate vital signs monitoring via PPG (3,14,27). However,
`Valencell’s PerformTek biometric algorithms actively pro-
`cess noisy body signals and extract accurate biometrics even
`during aggressive mnning and PA (23). These biometric sig-
`nals are tlnen combined with contextual accelerometry infor-
`mation within a statistical model to generate assessments of
`HR zone, calories burned, aerobic capacity (1702,“), and
`other pararnetes (17-21). A phone, computer, or other mo-
`bile device carn corrununicate directly with the microproces-
`sor via a Bluetooth link. In this particular study, the earbud
`sensor data wee streamed directly to a laptop via Bluetooth.
`Statistical methodology. A multiple linear regression
`model had been developed previously by Valencell to pro-
`vide a linear relation between estimated TEE, as predicted
`by the earbud sensor measurements, and the measured TEE,
`as recorded by the benchmark gas-exchange analysis de-
`vice. This linear model comprised fixed and time-varying
`terms. The fixed terms included weight (W), age (A), and
`sex (G) having a binary value of 0/1 for women/men,
`respectively. The time-varying terms included the earbud-
`estimated TEE GEB TEE) and the linear operations of real-
`time PPG and acceleometry (ACC). Although the details
`of the linear model are outside the scope of this article,
`the formalism of the resulting linear equation may be de-
`scribed by EB TEE =flg(PPG), h(ACC), W, A, G), where
`g and h are functions of PPG and ACC, respectively. It is
`important to note that this linear model was directed to-
`ward estimating TEE, and not the individual elements of
`resting energy expenditure (REE) or activity-related energy
`expenditure (AEE), 8 TEE is what is measured by the gas-
`exchange analysis.
`A separate model had been previously developed by
`Valencell
`to estimate \"02,mm based on the HR and
`accelerometry data collected during several prior rounds of
`CPX testing. The methodology behind this \702“lax esti-
`nnation is described elsewhere (18), and the equation
`follows the formalism of EB V02...“ = flMax HR,
`Min HR, k(ACC)), where EB \702...ax is the earbud-
`derived VOZmax, Max HR is the maximum reliable HR
`rrneasured by the earbud sensor, Min HR is the minimum
`reliable HR measured by the earbud sensor, and k is a
`finnction of the accelerometer readings measured through-
`out the CPX testing.
`
`1048
`
`Officnel Journal of the American Colege of Sports Medicine
`
`httpylwwwawn—mssenrg
`
`Copyright 9 2014 by the American College of Sports Medicine. Unauthorized reproduction of this article is prohibited.
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`140
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`that for this test, the benchmark ECG and the earbud HR
`are nearly identical throughout the run, such that they com-
`pletely overlap each other. Although complete overlap was
`not always observed, complete overlap was typically ob-
`served. Only rarely did the earbud or the ECG diverge to a
`substantial degree, as exemplified by the tight correlation
`shown in Figure 3. Also, on the rare occasions when diver-
`gence was observed, it was often attributable to either the
`earbud moving out of the ear or the ECG leads decoupl-
`ing from the subject’s skin. For the sake of objectivity in
`this study, all HR data points measured by the earbud and
`ECG sensors are shown in Figure 3, even for the case
`where earbud or ECG failures are subjectively believed to
`have occurred.
`
`A Bland—Altman plot comparing earbud-estimated HR
`(EB HR) versus the benchmark 12-lead ECG measured from
`the l4—person training group is presented in Figure 3. This
`figure illustrates the excellent agreement between EB HR
`and ECG throughout a full range of activity from rest to
`>200 bpm; the mean difference (bias) was -0.2%, the SD
`was i4.4%, and the coefficient of determination (R2) was
`0.98. In contrast with other reported optical HR measure-
`ment devices reported in literature (3,14,27), the EB HR
`measurement is quite robust throughout a full range of ac-
`tivity because the PerfonnTek biometric signal extraction
`algorithms are capable of characterizing motion noise during
`numerous activities and attenuating motion artifacts from the
`optieal signal in real time.
`In contrast with hip and pocket-wom pedometer-based
`approaches for ealculating distance (8,15,29), the PA level
`measured by the earbud prototype provides a good reference
`for body displacement during walking, jogging, and running
`without requiring knowledge of the user’s sex, height, age,
`weight, or fitness. Furtherrnore, neither a calibration re-
`gimen nor a GPS is required to tune parameters to the
`wearers’ gait. The earbud prototype distance measurement
`was highly accurate, with a bias of 0.3%, an SD of 4.2%,
`and an R2 of 0.93. This distance measurement was ob-
`
`tained through a novel transformation of three-axis acceler-
`ometer data, and its accuracy is aided by the sensor loeation
`at the ear.
`
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`Time (5)
`
`FIGURE 2 CPX testing output from a characteristic test. In this
`characteristic CPX test. the time of the progressive exercise test con-
`ducted on a standard treadmill is shown on the abscissa; the PA in-
`tensity using accelerometer counts in arbitrary units (a.u.; green line) is
`shown on the rightward ordinate; the HR in beats per minute from
`either the PerformTek earbud device (red) or from the simultaneously
`measured ECG (blue) is shown on the leflward ordinate. The earbud-
`determined HR and the ECG-memured HR show complete alignment
`in this exemplary characteristic test.
`
`After the l4-person training data study, the best-fitting
`coefficients for the TEE and V02...” models were deter-
`mined, and the resulting optimized equations were used in
`the nine-person validation data study to estimate TEE and
`\702max in real time. The resulting earbud-estimated values
`(EB TEE and EB (102......) were then compared with
`benchmark-measured values in accordance with the Bland—
`
`Altrnan plot (1,4).
`
`RESULTS
`
`HR. As previously described, the earbud measurements
`of HR and PA are part of the foundational formulas for
`EB TEE and EB V02...” Therefore, it is important that
`these measurements are accurate. An exemplary character-
`istic plot of real-time ECG, PerfonnTek HR, and h(ACC) for
`a subject undergoing a CPX test is shown in Figure 2. Note
`
`R-squared = 0.98
`
`
`
`
`SD = 4.4%
`
`t
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`50
`100
`150
`200
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`ECG heart rate (bpm)
`
`average HR (bpm)
`
`FIGURE 3 HR using the earbud (device under test |DUT|) and the simultaneously measured ECG benchmark. A. Regression relation comparing
`estimated (earbud) versus measured (ECG) HR for all data points collected for each participant. B. The Bland Altman plot of same. All the data
`points were taken from the training data collected during the Duke CI’X test. The mean dill'erence (bias) was 0.2% and the SD was 4.4%. The mean
`is shown by the green line. and the 1.96 SD (95% limits of agreement) boundaries are shown by the red lines.
`
`EARBUD—BASED SBISOR FOR PHYSICAL ASSESSMENTS
`
`Medic'ne & Science in Sports & Exerciseg
`
`1049
`
`Copyrigth 2014 by the American College of Sports Medicine. Unauthorized reproduction of this article is protibited.
`
`VALENCELL_0004
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`

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`2300
`150
`260
`250
`360
`350
`average calories (kcal)
`
`FIGURE 4 Bland Altman plots simultaneously comparing the rel'er-
`ernce CPX data and the earbud (device under test [DUT|) benchmark
`data collective. The Bland Altman plot of the dill'erence between the
`energy expenditure (EE; kcal) using the reference CPX test and the
`DUT benchmark EE measurements taken from the l4-person CPX
`training group. The mean is shown by the green line, and the 1.96 SD
`(95% limits of agreement) boundaries are shown by the red lines.
`
`TEE. The EB TEE closely estimated the benchmark TEE
`for the training group data set, with a bias of -0.7% and
`an SD of i7.4% (Fig. 4). The correlation between the
`EB TEE and the benchmark TEE for the validation group
`data set was identical with that of the training data set, with
`a bias of —0.7%, an SD of i7.4%, and an R2 coefficient of
`0.86 (Fig. 4).
`VOzmx. The EB V02...” closely estimated the bench-
`mark ‘70sz for the training group data set, with a bias
`of —o.1%, an so of 3.7%, and an R2 coefficient of 0.36
`(Fig. 5). The correlation between the EB \"Ozfllax and the
`benchmark TEE for the validation group data set was simi-
`lar to that of the training data set, with a bias of —3.2% and
`an SD of i7.3%.
`
`DISCUSSION
`
`To satisfy commercial, clinical, and research oriented
`markets for personal energy balance monitoring, a wear-
`able sensor module must satisfy four key criteria. The sensor
`module must be 1) seamless with daily living (comfortable,
`convenient, and socially acceptable), 2) sufficiently accu-
`rate for multiple life activities (indoors and outdoors), 3)
`
`able to provide understandable, actionable, and motivational
`feedback to the user, and 4) autonomous and user—fiiendly.
`Today, a variety of commercially available products offer
`step counting and estimated calorie monitoring. Many of
`these solutions have provided value to researchers study-
`ing energy balance and to fitness professionals and clini-
`clans monitoring the progress of exercise and diet plans.
`However, none of these products satisfy all the previously
`mentioned criteria, limiting the effectiveness and scope of
`l) long-term clinical research on energy balance research
`and 2) healtln and fitness solutions for end users. In con-
`trast, newly developed enr’oud sensor technology offers the
`promise of meeting these needs, enabling a truly seamless
`energy balance—monitoring platform for use in clinical re-
`search, consumer fitness, clinieal assessment of energy bal-
`ance, and mobile health magenent.
`The feasibility has been established for the highly minia-
`turized, noninvasive optical enrbud sensor technology for
`accurately monitoring physiological metrics such as HR,
`TEE, and maximum oxygen consumption (VObnax) through
`the ear. The earbud sensor accurately predicted HR through-
`out all activity levels investigated, from rest to peak perfor-
`mance, with a mean difference (bias) of -0.2% arnd an SD
`of i4.4% when compared with an ECG benchmark device.
`Furthermore, real-time algorithms within the earbud sensor
`accurately predicted (a) TEE with a bias of —0.7% and an
`SD of i7.4% arnd (b) VOZmax with a bias of —3.2% and an
`SD of i7.3%. This particular evaluation did not address
`user comfort or battery life but a commercially available
`Bluetootln audio headset, the iliverON'M, incorporating the
`evaluated PeformTek® biometric sensor technology adver-
`tises seveal hours of measurement time while also support-
`ing music.
`The excellent performance of the earbud sensor for ac-
`curately measuring HR throughout extreme PA is especially
`noteworthy. Motion artifacts have been the greatest limita-
`tion to the continuous monitoring of vital sigrns during ac-
`tivity (3,14,27), and the ability to accurately monitor vital
`sigrns with a consumer-priced audio headset is particularly
`irnpactful to public health.
`
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`FIGURE 5 Bland Altman plots simultaneously comparing reference CPX data and earbud (device under test IDUTI) benchmark data collective. A.
`Distance traveled. B. V01.“ (mL-kg l-min l); [E (kcal). Nine subjects were studied in this CPX validation group study. The mean is shown by the
`green line. and the 1.96 SD (95% limits of agreement) boundaries are shown by the red lines.
`
`1050 Official Journal of the American Colege of Sports Medicine
`
`http1/wwwawn—msseorg
`
`Copyright c 2014 by the American College of Sports Medicine. Unauthorized reproduction of this article is prolibited.
`
`VALENCELL_0005
`
`

`

`TEE and VOL,max in a larger cohort group having a broader
`range of aerobic capacity, ranging from approximately 35
`to 70 mL-kg_'-min_'. Furtlremrore,
`the earbud sensor
`should be put to the test of estimating TEE during a broader
`set of activities than simple treadmill testing, using the
`standard doubly labeled water (DLW) methodology as a
`benchmark. In addition, the ability of the earbud sensor to
`estimate the resting metabolic rate (REE) of subjects should
`also be assessed.
`
`Improving the accuracy of these assessments will rely on
`1) optimizing algorithms based on a larger study sample of
`subjects exercising in more diverse errviromnents (such as
`daily life activities) and 2) adding additional biometrics to
`the predictive algorithms
`for energy expenditure and
`V02m. Valencell’s PerformTek earbud sensor is comprised
`mostly of novel optomecharrics and signal extraction algo-
`rithms. The accuracy of the HR algorithms is so high,
`approaching machine error, such that it is unlikely that ad-
`ditional improvements urn be made in the optomecharrical
`sensor module for accuracy. Rather, advancements are likely
`to arise fiom the development of an optimized statistical
`model that incorporates personalized REE estimations into
`the model. The algorithms for estimating personalized REE
`ean be developed by evaluating the PPG profile of subjects
`at rest with a gas-exchange analysis batchmark (REE test-
`ing) and by identifying new blood flow profile features that
`correlate with the gas-exchange analysis data.
`
`The validation test'ng in this reseaeh was funded in put by the
`National Institutes of Health via Phwe I SBIR 1 R430K083141-01A1 .
`There are no conflicts of interest.
`The results of the present study do not constitute endorsement by
`the American College of Sports Medic’rre.
`
`>UU Cm Um Dm 2mmm
`
`9. Daniels IT. A physiologist‘s view of mrrrring economy. Med Sci
`Sports Exerc. I985;17(3)‘332 8.
`10. Funk KL, Stevens VJ, Appel Ll. Amociations of Internet website
`use with weiyrt change in a long term weight loss maintenance
`program. J Med Internet Res. 2010;12(3):e29.
`11. Gallup Healthways Well Being Index. 2012. Available from:
`http://www.gallrrp.comlpolll157505/americam exercising sligrtly
`2012.aspx
`12. Harr's C, Debeliso M, Adams K]. The effects of rrmrring speed
`on the metabolic and mechanical energy costs of running. .I Exerc-
`Physio]. 2003;6(3):28 37.
`13. Headphonm: Ownership & Application Stud), 2012. The NPD
`Group. August 2012. p. 4.
`I4. Jiang HH, Asada HH, Gibbs P. Active noise cancellation using
`MEMS accelerometers for motion tolerant wearable bioserrsors.
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`In: Conference Proceedings ofthe IEEE Engineering in Medicine
`and Biology Society. 2004. pp. 2157 60.
`15. Kong YC, Ming S. Improving energy expenditure estimation by
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`16. Krakow G. Smartphone sales to top 1 billion this year. The
`Street. 2013.
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`17. LeBoeuf SF, Tucker JB, Aumer ME. Light Guiding Devica and
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`
`When compared with the approaches in HR chest strap
`for estimating energy expenditure (5,6), the earbud sensor
`algorithms for estimating TEE and VOZ“flax are also note-
`worthy. It is likely that the fixed lomtion of the earbud
`with respect to the spine increases the accuracy of activity
`measurements, which feed the TEE and the V02...“ models.
`However, some important limitations of these algorithms
`are of note. First, these algorithms have demonstrated sub-
`stantial efficacy in estimating TEE and \"02.max under
`walking, jogging, and running conditions, conditions com-
`mon for clinical CPX evaluations. However, it is yet to be
`determined how accurate these algorithms will be at esti-
`mating these parameters during everyday life activities and
`other exercising regimens, such as weightlifting, swimming,
`contact sports, daily household activities, and the like. There
`are several strrdies emphasizing the importance of eaution
`when applying V02 estimation models to universal PA
`(9,12,22,25,28,30,32,33). Moreover, it is not clear how well
`these algorithms will predict REE or energy expended during
`sedentary activity, where caloric expenditure is dominated
`by the metabolic rate of the individual as opposed to PA.
`Second,
`the V02“, range of participants in the valida-
`tion study was relatively small: fiom approximately 50 to
`65 mL-kg_'-min_' (Fig. 5). Indeed, the relatively low R2
`coefficient for estimated VOZmm and the ostarsible non-
`linear bias of Figure SB together suggest that the accuracy
`of the V02...“ model cannot be affirmed with the current
`data set. Finally, to be clinieally relevant, the predictions for
`TE and V02”, would ideally be even more accurate, with
`the goal of an SD of less than i5%.
`To address these areas for improvement, future work
`should evaluate the efficacy of the earbud sensor at estimating
`
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