`
`EXHIBIT 2001
`EXHIBIT 2001
`
`

`

`Earbud-Based Sensorfor the Assessment
`of Energy Expenditure, HR, and VOs,n,ax
`
`STEVEN FRANCIS LEBOEUF', MICHAEL E. AUMER!, WILLIAM E. KRAUS?, JOHANNAL. JOHNSON?,
`and BRIAN DUSCHA?
`
`‘Valencell, Inc., Raleigh, NC; and ?Division 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. Earbud-Based Sensor for the Assessment of
`Energy Expenditure, HR, and VOomax. Med. Sci. Sports Exerc., Vol. 46, No. 5, pp. 1046 1052, 2014. Introduction/Purpose: The goal
`of this program was to determinethefeasibility of a novel noninvasive, highly miniaturized optomechanical earbud sensorfor accurately
`estimating total energy expenditure (TEE) and maximum oxygen consumption (VO22x). The optomechanical sensor module, small
`enough to fit inside commercial audio earbuds, was previously developed to provide a seamless way to measure blood flow informa-
`tion during daily life activities. The sensor module was configured to continuously measure physiological information via photo-
`plethysmography and physical activity information via accelerometry. This information was digitized and sent to a microprocessor
`where digital signal-processing algorithms extract physiological metrics in real time. These metrics were streamed wirelessly from
`the earbud 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 same exercise measurementprotocol consisting of treadmill-
`based cardiopulmonary exercise testing to reach VO2max. Benchmark sensors included a 12-lead ECG sensor for measuring HR, a
`calibrated treadmill for measuring distance and speed, and a gas-exchange analysis instrument for measuring TEE and VO2max. The
`earbud sensor was the device under test. Benchmark and device under test data collected from the 14-person training data set study
`were integrated into a preconceived statistical model for correlating benchmark data with earbud sensor data. Coefficients were op-
`timized, and the optimized model was validated in the 9-person validation data set. Results: It was observed that the earbud sensor
`estimated TEE and VO>,,.x With mean + SD percentestimation errors of 0.7+7.4% and
`3.2 + 7.3%, respectively. Conclusion: The
`earbud sensor can accurately estimate TEE and VO,,. during cardiopulmonary exercise testing. Key Words: EAR, ACCELEROMETER,
`PHOTOPLETHYSMOGRAPHY, PULSE
`
`odifiable health risk factors, such as high stress,
`poordiet, and sedentary lifestyle, account for 25%
`of all medical expenses and millions of deaths
`per year worldwide (2). The U.S. 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 (11), spending more than $55
`billion in weight loss programs and more than $17 billion
`on fitness products (31). The disconnect between dollars
`spent on weightloss and obesity levels may be explained by
`recent findings that traditional diets do not work (24) alone
`
`Address for correspondence: Steven Francis LeBoeuf, Ph.D., Valencell, Inc.,
`2800-154 Sumner Blvd., Raleigh, NC 27616; E-mail: LeBoeuf@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
`DOI: 10.1249/MSS.0000000000000183
`
`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.
`Weightloss programs aimed at promoting fitness through
`direct measurementof 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 maintenanceof weight loss in long-term diet/weight
`managementstudies (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 VO>,calories bumed, and VOrmax-
`Indeed, there is a clear opportunity to encourage a broader
`population to embraceactive lifestyles by integrating mobile
`fitness monitoring devices with compelling user experiences.
`However, compelling user experiences must be meaningful,
`and to be meaningful, the fitness monitoring gadgets must
`provide information thatis sufficiently accurate to be action-
`able. This goal is challenged by the fact that commercial pe-
`dometers are inaccurate by greater than + 20% in reporting
`calories burned (8,29).
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`maximum oxygen consumption (VO},,ax), and this study is
`reported herein.
`
`METHODS
`
`
`
`FIGURE 1 The components andsize of the device under test (DUT).
`Shown are the earbud and the medallion containing the majority of
`the computational components. Shownfor scale is a U.S. quarter. Note
`the position of the sensor module at the bottom of the antitragus. The
`sensor moduleis configured to fit between the concha andthe antitragus
`of the ear.
`
`To overcomethese reported limitations, an earbud sensor
`module—as opposed to an ActiGraph wrist-, arm-, or leg-
`worm 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 photoplethysmography (PPG)
`and changes in body motion through a three-axis acceler-
`ometer. This sensor module was designed 1) 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 from the PPG
`signal and to continuously generate estimates of HR and
`VO, metrics in real time based ona statistical model com-
`prising PPG and accelerometry information. The DSP was
`in electrical communication with a Bluetooth chipsetso that
`the real-time metrics could be called upon byaclient de-
`vice (such as a laptop or smartphone). A preliminary feasi-
`Recent energy expenditure studies, using a wearable Acti-
`bility study of this PerformTek” earbud sensor module had
`Health chest strap monitor for measuring both PA and HR,
`have demonstrated greater accuracy (5,6). These researchers
`previously demonstrated accurate HR measurements during
`achieved such predictive accuracy through branched equation
`exercise, thus potentially eliminating the need for an elec-
`modeling, using HR information and accelerometry infor-
`trocardiographic chest strap in many use cases. This was a
`mation as independent variables. Although these findings are
`critical finding for the issue of user compliance, as 58% of
`quite encouraging, researchers using the ActiHealth monitor
`U.S. headphone owners listen to headphones while exercis-
`point out several shortcomings. First, despite the relatively
`ing and 34% wear headphones during everydaylife activities
`high precision achievable through branched equation model-
`(such as doing work around the house) (13), 10 times greater
`ing, poorer accuracyis observed if individual calibrations are
`than the number ofAmericans whoexercise with chest straps.
`Subjects. In this study, 23 subjects of good physical
`not used (5,6). This means that the wearable monitors must
`be calibrated for each user, in a process that is both time con-
`health were divided into a training group of 14 subjects and
`a validation group of9 subjects. This samplesizeis justified
`suming and burdensome. Furthermore, as audio earbuds are
`by the high “effect size” observed for calibrated correla-
`packaged with smartphones and digital media players that
`tions of VO, and HR (22) and is further supported by the
`are sold in volumes of hundreds of millions of units a year
`very high R? coefficient observed (23) when comparing
`(16), the audio earbud form factor provides the opportunity
`the earbud-determined HR to 12-lead ECG-measured HR
`to reach a larger consumer audience than that of an HR
`chest strap, which is sold in volumes ofless than 10 million
`during exercise. The training group (Table la) comprised
`12 men and 2 women:age = 39 + 11.8 yr, weight = 73.5 +
`per year.
`12.2 kg, height = 69 + 2.9 cm, BMI = 23.6 + 2.1 kgm”.
`The goal of this study was to determine the feasibility of
`a highly miniaturized, noninvasive optomechanical earbud
`The validation group (Table 1b) comprised five men and
`four women: age = 36 + 6.9 yr, weight = 67.6 + 15.7 kg,
`sensor technology for accurately monitoring physiological
`height = 173 + 7.4 cm, BMI = 22.3 + 4.0 kgm”. Each
`metrics such as HR, total energy expenditure (TEE), and
`
`TABLE 1. Descriptive characteristics (mean + SD) of(a) training group and (b) validation group.
`(a) Training Group
`
`(b) Validation Group
`
`Parameter
`Sex
`Age
`Weight
`Height
`Distance
`Energy expenditure
`Maximum VO.
`BMI
`
`Value (Mean + SD)
`2 females, 12 males
`30 + 11.8 yr
`73.5 + 12.2 kg
`175+7.4cm
`2.95 + 0.5 km
`213 + 47.8 kcal
`55.9+6.5mLkg ‘min '
`23.6 + 2.1kg-m ?
`
`Parameter
`Sex
`Age
`Weight
`Height
`Distance
`Energy expenditure
`Maximum V0.
`BMI
`
`Value (Mean + SD)
`4 females, 5 males
`36 + 6.9 yr
`67.6 + 15.7 kg
`173 + 7.4. cm
`2.80 + 0.3 km
`178 + 51.5 kcal
`55.1+5.5 mL-kg ‘min *
`22.3 + 4.0 kg-‘m ”
`
`EARBUD-BASED SENSOR FOR PHYSICAL ASSESSMENTS
`
`Medicine & Science in Sports & Exercise
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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 VOomax. The achievement
`of VOxmax Was determined by reaching at least two of the
`three followingcriteria: plateau in VO> overthe last minute
`of exercise, achievement ofat least 1.10 RER, and achieve-
`mentofatleast 17 in perceived exertion on the Borg scale. The
`mean + SD VOomax Values ofthe training group andthe vali-
`dation group were 55.9 + 6.5 and 55.1 + 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 VOzmax- The earbud sensor served as the
`device under test. All subjects provided informed consent
`as approved bythe investigational 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 benchmark for HR, and a
`TrueMax 2400 ParvoMedics (ParvoMedics, Sandy, UT) gas-
`exchange analysis mouthpiece was used as a benchmark for
`energy expenditure and continuous measures of VO. 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 positionin a reclining chair for a few minutes while
`wearing the benchmark equipment and earbud sensor. After
`the resting period, subjects were instructed to move from the
`chair to the calibrated treadmill and execute the CPX testing
`with graded intensity ranging from 0 to 9.1 mph speeds. The
`protocol used consisted of 2-min stages, increasing the work-
`load by approximately one metabolic equivalent per stage.
`Measurements from the benchmark sensors and earbud sen-
`sor were collected continuously throughoutthe treadmill run.
`Participants were asked to continue running during each in-
`creasing speed until they were completely exhausted. The
`last 40 s of benchmark gas-exchange analysis data were av-
`eraged to determine measured peak VO>.
`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, and 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 andthe antitragus of each subject upon
`earbud placement. The right and the left earbuds were de-
`signed to be pluggable to a wireless Bluetooth “medallion”
`via a detachable connector (as shown in Fig. 1). The medal-
`lion housed the microprocessor and the Bluetooth chipset.
`At the heart of this noninvasive earbud sensoris a highly
`miniaturized optomechanical module (17—21,23) that mea-
`sures optical 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
`optomechanical housing. Designed tofit flush with the body
`of a standard audio earbud, the earbud essentially maintains
`the form factor of a typical audio earbud and does notre-
`quire an ear clip or an in-ear-canal sensor to function.
`The optical and mechanical information collected from
`the ear 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 firm-
`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 algorithmsactively pro-
`cess noisy bodysignals and extract accurate biometrics even
`during aggressive running and PA (23). These biometric sig-
`nals are then combined with contextual accelerometry infor-
`mation within a statistical model to generate assessments of
`HR zone, calories bumed, aerobic capacity (VO2max), and
`other parameters (17-21). A phone, computer, or other mo-
`bile device can communicate directly with the microproces-
`sor via a Bluetooth link. In this particular study, the earbud
`sensor data were 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 (EB TEE) andthe linear operations of real-
`time PPG and accelerometry (ACC). Although the details
`of the linear model are outside the scope ofthis article,
`the formalism of the resulting linear equation may be de-
`scribed by EB TEE = f(g(PPG), h(ACC), W, A, G), where
`g and / 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), as TEE is what is measured by the gas-
`exchangeanalysis.
`A separate model had been previously developed by
`Valencell
`to estimate VOomax based on the HR and
`accelerometry data collected during several prior rounds of
`CPX testing. The methodology behind this VO2max esti-
`mation is described elsewhere (18), and the equation
`follows the formalism of EB VOomax = /(Max HR,
`Min HR, k(ACC)), where EB VOzmax is the earbud-
`derived VOomax, Max HR is the maximum reliable HR
`measured by the earbud sensor, Min HR is the minimum
`reliable HR measured by the earbud sensor, and k is a
`function of the accelerometer readings measured through-
`out the CPX testing.
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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
`shownin 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 ECGfailures 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 14-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 +4.4%, and the coefficient of determination (R”) was
`0.98. In contrast with other reported optical HR measure-
`ment devices reported in literature (3,14,27), the EB HR
`measurementis quite robust throughout a full range of ac-
`tivity because the PerformTek biometric signal extraction
`algorithms are capable of characterizing motion noise during
`numerous activities and attenuating motion artifacts from the
`optical signal in realtime.
`In contrast with hip and pocket-worn pedometer-based
`approaches for calculating 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. Furthermore, neither a calibration re-
`gimen nor a GPSis 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 R° 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 location
`at the ear.
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`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 arbitraryunits (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 leftward ordinate. The earbud-
`determined HR and the ECG-measured HR show complete alignment
`in this exemplary characteristic test.
`
`After the 14-person training data study, the best-fitting
`coefficients for the TEE and VOxmax models were deter-
`mined, and the resulting optimized equations were used in
`the nine-person validation data study to estimate TEE and
`VOrmax in real time. The resulting earbud-estimated values
`(EB TEE and EB VOomax) were then compared with
`benchmark-measured values in accordance with the Bland—
`Altman plot (1,4).
`
`RESULTS
`
`HR.Aspreviously described, the earbud measurements
`of HR and PA are part of the foundational formulas for
`EB TEE and EB VOomax. Therefore, it is important that
`these measurements are accurate. An exemplary character-
`istic plot ofreal-time ECG, PerformTek HR, and h(ACC)for
`a subject undergoing a CPXtest is shownin Figure 2. Note
`
`R-squared = 0.98
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`HRusing the earbud (device under test [DUT]) and the simultaneously measured ECG benchmark. A. Regression relation comparing
`FIGURE 3
`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 CPX test. The mean difference (bias) was 0.2% and the SD was 4.4%. The mean
`is shown bythe green line, and the 1.96 SD (95% limits of agreement) boundaries are shown bythe red lines.
`
`EARBUD-BASED SENSOR FOR PHYSICAL ASSESSMENTS
`
`Medicine & Science in Sports & Exercises
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`1049
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`

`able to provide understandable, actionable, and motivational
`feedback to the user, and 4) autonomousand user-friendly.
`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-
`cians 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
`1) long-term clinical research on energy balance research
`and 2) health and fitness solutions for end users. In con-
`trast, newly developed earbud sensor technology offers the
`promise of meeting these needs, enabling a truly seamless
`energy balance-monitoring platform for use in clinical re-
`search, consumerfitness, clinical assessment of energy bal-
`ance, and mobile health management.
`The feasibility has been established for the highly minia-
`turized, noninvasive optical earbud sensor technology for
`accurately monitoring physiological metrics such as HR,
`TEE, and maximum oxygen consumption (VO2max) 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% and an SD
`of +4.4% 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 +7.4% and (b) VOomax With a bias of —3.2% and an
`SD of +7.3%. This particular evaluation did not address
`user comfort or battery life but a commercially available
`Bluetooth audio headset, the iriverON™, incorporating the
`evaluated PerformTek” biometric sensor technology adver-
`tises several hours of measurement time while also support-
`ing music.
`The excellent performance of the earbud sensor for ac-
`curately measuring HR throughout extreme PAis especially
`noteworthy. Motion artifacts have been the greatest limita-
`tion to the continuous monitoring ofvital signs during ac-
`tivity (3,14,27), and the ability to accurately monitor vital
`signs with a consumer-priced audio headset is particularly
`impactful to public health.
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`250
`300
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`
`FIGURE 4 Bland Altmanplots simultaneously comparing the refer-
`ence CPX data and the earbud (device under test [DUT]) benchmark
`data collective. The Bland Altmanplot of the difference between the
`energy expenditure (EE; kcal) using the reference CPX test and the
`DUT benchmark EE measurements taken from the 14-person CPX
`training group. The mean is shown bythe green line, and the 1.96 SD
`(95% limits of agreement) boundaries are shown bythe 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 +7.4% (Fig. 4). The correlation between the
`EB TEEandthe benchmark TEEfor the validation group
`data set was identical with thatof the training data set, with
`a bias of —0.7%, an SD of 7.4%, and an R? coefficient of
`0.86 (Fig. 4).
`VO2max- The EB VOxmax closely estimated the bench-
`mark VOomax for the training group data set, with a bias
`of —0.1%, an SD of +8.7%, and an R° coefficient of 0.36
`(Fig. 5). The correlation between the EB VO2max and the
`benchmark TEEfor the validation group data set was simi-
`lar to that ofthe training data set, with a bias of —3.2% and
`an SD of 7.3%.
`
`DISCUSSION
`
`To satisfy commercial, clinical, and research oriented
`markets for personal energy balance monitoring, a wear-
`able sensor module mustsatisfy 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)
`
`
`
`1.5
`2
`2.5
`average distance (miles)
`
`
`40
`60
`80
`average VO2max (mi/kg/min)
`
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`FIGURES Bland Altman plots simultaneously comparing reference CPX data and earbud (device undertest [DUT]) benchmarkdatacollective. A.
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`green line, and the 1.96 SD (95% limits of agreement) boundaries are shown bythe red lines.
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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 VOzmax are also note-
`worthy. It is likely that the fixed location of the earbud
`with respect to the spine increases the accuracy ofactivity
`measurements, which feed the TEE and the VO2max models.
`However, some important limitations of these algorithms
`are of note. First, these algorithms have demonstrated sub-
`stantial efficacy in estimating TEE and VOomax 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 everydaylife activities and
`other exercising regimens, such as weightlifting, swimming,
`contact sports, daily household activities, and the like. There
`are several studies emphasizing the importance of caution
`when applying VO, estimation models to universal PA
`(9,12,22,25,28,30,32,33). Moreover, it is not clear how well
`these algorithmswill 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 VOomax range of participants in the valida-
`tion study was relatively small: from approximately 50 to
`65 mL‘kg'min™' (Fig. 5). Indeed, the relatively low R°
`coefficient for estimated VOrmax and the ostensible non-
`linear bias of Figure 5B together suggest that the accuracy
`of the VOrmax model cannot be affirmed with the current
`data set. Finally, to be clinically relevant, the predictions for
`TEE and VOomax would ideally be even more accurate, with
`the goal of an SD ofless than +5%,
`To address these areas for improvement, future work
`should evaluate the efficacy ofthe earbud sensor at estimating
`
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`
`TEE and VO>,,ax in a larger cohort group having a broader
`range of aerobic capacity, ranging from approximately 35
`to 70 mL-kg'min™'. Furthermore,
`the earbud sensor
`should be put to the test ofestimating TEE during a broader
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`VO>max- Walencell’s PerformTek earbud sensoris comprised
`mostly of novel optomechanics 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 can be made in the optomechanical
`sensor module for accuracy. Rather, advancements arelikely
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`at rest with a gas-exchange analysis benchmark (REEtest-
`ing) and by identifying new blood flow profile features that
`correlate with the gas-exchange analysis data.
`
`The validation testing in this research was funded in part by the
`National Institutes of Health via Phase | SBIR 1R43DK083141-01A1.
`There are no conflicts of interest.
`Theresults of the present study do not constitute endorsement by
`the American College of Sports Medicine.
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