Modifiable health risk factors, such as high stress, poor diet, 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 weight loss and obesity levels may be explained by recent findings that traditional diets do not work (24) alone to prevent weight gain and to promote weight loss. Dietary measure must be combined with energy expenditure to accomplish 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 feedback with an online user experience correlates with improved 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 feedback about energy expenditure and aerobic fitness level, such as V˙O2, calories burned, and V˙O2max.
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 meaningful, and to be meaningful, the fitness monitoring gadgets must provide information that is sufficiently accurate to be actionable. This goal is challenged by the fact that commercial pedometers are inaccurate by greater than ± 20% in reporting calories burned (8,29).
Recent energy expenditure studies, using a wearable ActiHealth 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 accelerometry information 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 modeling, poorer accuracy is observed if individual calibrations are not used (5,6). This means that the wearable monitors must be calibrated for each user, in a process that is both time consuming and burdensome. 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 (V˙O2max), and this study is reported herein.
To overcome these reported limitations, an earbud sensor module—as opposed to an ActiGraph wrist-, arm-, or leg-worn 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 accelerometer. 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 removing motion artifacts and environmental noise from the PPG signal and to continuously generate estimates of HR and V˙O2 metrics in real time based on a statistical model comprising 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 device (such as a laptop or smartphone). A preliminary feasibility study of this PerformTek® earbud sensor module had previously demonstrated accurate HR measurements during exercise, thus potentially eliminating the need for an electrocardiographic chest strap in many use cases. This was a critical finding for the issue of user compliance, as 58% of U.S. headphone owners listen to headphones while exercising and 34% wear headphones during everyday life activities (such as doing work around the house) (13), 10 times greater than the number of Americans who exercise with chest straps.
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 calibrated correlations of V˙O2 and HR (22) and is further supported by the very high R2 coefficient observed (23) when comparing the earbud-determined HR to 12-lead ECG-measured HR during exercise. The training group (Table 1a) comprised 12 men and 2 women: age = 39 ± 11.8 yr, weight = 73.5 ± 12.2 kg, height = 69 ± 2.9 cm, BMI = 23.6 ± 2.1 kg·m−2. The validation group (Table 1b) comprised five men and four women: age = 36 ± 6.9 yr, weight = 67.6 ± 15.7 kg, height = 173 ± 7.4 cm, BMI = 22.3 ± 4.0 kg·m−2. Each subject underwent the same exercise measurement protocol, including a treadmill-based cardiopulmonary exercise (CPX) test, at 0° incline, to reach V˙O2max. The achievement of V˙O2max was determined by reaching at least two of the three following criteria: plateau in V˙O2 over the last minute of exercise, achievement of at least 1.10 RER, and achievement of at least 17 in perceived exertion on the Borg scale. The mean ± SD V˙O2max values of the training group and the validation group were 55.9 ± 6.5 and 55.1 ± 5.5 mL·kg−1·min−1, 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 measuring TEE and V˙O2max. The earbud sensor served as the device under test. All subjects provided informed consent as approved by the investigational review board of the Duke University School of Medicine.
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 V˙O2. The benchmark sensors were calibrated according to the standard maintenance guidelines of the manufacturers. The subjects were then fitted with an earbud sensor (Fig. 1) powered by the aforementioned PerformTek physiological monitoring 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. 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 workload by approximately one metabolic equivalent per stage. Measurements from the benchmark sensors and earbud sensor were collected continuously throughout the treadmill run. Participants were asked to continue running during each increasing speed until they were completely exhausted. The last 40 s of benchmark gas-exchange analysis data were averaged to determine measured peak V˙O2.
The novel noninvasive earbud sensor (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 and the antitragus of each subject upon earbud placement. The right and the left earbuds were designed to be pluggable to a wireless Bluetooth “medallion” via a detachable connector (as shown in Fig. 1). The medallion housed the microprocessor and the Bluetooth chipset.
At the heart of this noninvasive earbud sensor is a highly miniaturized optomechanical module (17–21,23) that measures 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 to fit flush with the body of a standard audio earbud, the earbud essentially maintains the form factor of a typical audio earbud and does not require 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 firmware 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 accurate vital signs monitoring via PPG (3,14,27). However, Valencell’s PerformTek biometric algorithms actively process noisy body signals and extract accurate biometrics even during aggressive running and PA (23). These biometric signals are then combined with contextual accelerometry information within a statistical model to generate assessments of HR zone, calories burned, aerobic capacity (V˙O2max), and other parameters (17–21). A phone, computer, or other mobile device can communicate directly with the microprocessor via a Bluetooth link. In this particular study, the earbud sensor data were streamed directly to a laptop via Bluetooth.
A multiple linear regression model had been developed previously by Valencell to provide 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 device. 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) and the linear operations of real-time PPG and accelerometry (ACC). Although the details of the linear model are outside the scope of this article, the formalism of the resulting linear equation may be described by EB_TEE = f(g(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 toward 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-exchange analysis.
A separate model had been previously developed by Valencell to estimate V˙O2max based on the HR and accelerometry data collected during several prior rounds of CPX testing. The methodology behind this V˙O2max estimation is described elsewhere (18), and the equation follows the formalism of EB_V˙O2max = f(Max_HR, Min_HR, k(ACC)), where EB_V˙O2max is the earbud-derived V˙O2max, 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 throughout the CPX testing.
After the 14-person training data study, the best-fitting coefficients for the TEE and V˙O2max models were determined, and the resulting optimized equations were used in the nine-person validation data study to estimate TEE and V˙O2max in real time. The resulting earbud-estimated values (EB_TEE and EB_V˙O2max) were then compared with benchmark-measured values in accordance with the Bland–Altman plot (1,4).
As previously described, the earbud measurements of HR and PA are part of the foundational formulas for EB_TEE and EB_V˙O2max. Therefore, it is important that these measurements are accurate. An exemplary characteristic plot of real-time ECG, PerformTek HR, and h(ACC) for a subject undergoing a CPX test is shown in Figure 2. Note that for this test, the benchmark ECG and the earbud HR are nearly identical throughout the run, such that they completely overlap each other. Although complete overlap was not always observed, complete overlap was typically observed. 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 divergence was observed, it was often attributable to either the earbud moving out of the ear or the ECG leads decoupling 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 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 (R2) was 0.98. In contrast with other reported optical HR measurement devices reported in literature (3,14,27), the EB_HR measurement is quite robust throughout a full range of activity 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 real time.
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 regimen 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 obtained through a novel transformation of three-axis accelerometer data, and its accuracy is aided by the sensor location at the ear.
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_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 ±7.4%, and an R2 coefficient of 0.86 (Fig. 4).
The EB_V˙O2max closely estimated the benchmark V˙O2max for the training group data set, with a bias of −0.1%, an SD of ±8.7%, and an R2 coefficient of 0.36 (Fig. 5). The correlation between the EB_V˙O2max and the benchmark TEE for the validation group data set was similar to that of the training data set, with a bias of −3.2% and an SD of ±7.3%.
To satisfy commercial, clinical, and research oriented markets for personal energy balance monitoring, a wearable sensor module must satisfy four key criteria. The sensor module must be 1) seamless with daily living (comfortable, convenient, and socially acceptable), 2) sufficiently accurate for multiple life activities (indoors and outdoors), 3) able to provide understandable, actionable, and motivational feedback to the user, and 4) autonomous and 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 studying energy balance and to fitness professionals and clinicians 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 contrast, newly developed earbud sensor technology offers the promise of meeting these needs, enabling a truly seamless energy balance–monitoring platform for use in clinical research, consumer fitness, clinical assessment of energy balance, and mobile health management.
The feasibility has been established for the highly miniaturized, noninvasive optical earbud sensor technology for accurately monitoring physiological metrics such as HR, TEE, and maximum oxygen consumption (V˙O2max) through the ear. The earbud sensor accurately predicted HR throughout all activity levels investigated, from rest to peak performance, 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) V˙O2max 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 advertises several hours of measurement time while also supporting music.
The excellent performance of the earbud sensor for accurately measuring HR throughout extreme PA is especially noteworthy. Motion artifacts have been the greatest limitation to the continuous monitoring of vital signs during activity (3,14,27), and the ability to accurately monitor vital signs with a consumer-priced audio headset is particularly impactful to public health.
When compared with the approaches in HR chest strap for estimating energy expenditure (5,6), the earbud sensor algorithms for estimating TEE and V˙O2max are also noteworthy. It is likely that the fixed location of the earbud with respect to the spine increases the accuracy of activity measurements, which feed the TEE and the V˙O2max models. However, some important limitations of these algorithms are of note. First, these algorithms have demonstrated substantial efficacy in estimating TEE and V˙O2max under walking, jogging, and running conditions, conditions common for clinical CPX evaluations. However, it is yet to be determined how accurate these algorithms will be at estimating these parameters during everyday life activities and other exercising regimens, such as weight lifting, swimming, contact sports, daily household activities, and the like. There are several studies emphasizing the importance of caution when applying V˙O2 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 V˙O2max range of participants in the validation study was relatively small: from approximately 50 to 65 mL·kg−1·min−1 (Fig. 5). Indeed, the relatively low R2 coefficient for estimated V˙O2max and the ostensible nonlinear bias of Figure 5B together suggest that the accuracy of the V˙O2max model cannot be affirmed with the current data set. Finally, to be clinically relevant, the predictions for TEE and V˙O2max would ideally be even more accurate, with the goal of an SD of less than ±5%.
To address these areas for improvement, future work should evaluate the efficacy of the earbud sensor at estimating TEE and V˙O2max in a larger cohort group having a broader range of aerobic capacity, ranging from approximately 35 to 70 mL·kg−1·min−1. Furthermore, 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 environments (such as daily life activities) and 2) adding additional biometrics to the predictive algorithms for energy expenditure and V˙O2max. Valencell’s PerformTek earbud sensor is comprised mostly of novel optomechanics and signal extraction algorithms. The accuracy of the HR algorithms is so high, approaching machine error, such that it is unlikely that additional improvements can be made in the optomechanical sensor module for accuracy. Rather, advancements are likely to arise from the development of an optimized statistical model that incorporates personalized REE estimations into the model. The algorithms for estimating personalized REE can be developed by evaluating the PPG profile of subjects at rest with a gas-exchange analysis benchmark (REE testing) 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 I SBIR 1R43DK083141-01A1. There are no conflicts of interest.
The results of the present study do not constitute endorsement by the American College of Sports Medicine.
1. Altman DG, Bland JM. Measurement in medicine: the analysis of method comparison studies. Statistician
. 1983; 32 (3): 307–17.
2. Anderson DR, Whitmer RW, Goetzel RZ, et al. The relationship between modifiable health risks and group-level health care expenditures. Am J Health Promot
. 2000; 15 (1): 45–52.
3. Asada HH, Shaltis P, Reisner A, Sokwoo R, Hutchinson RC. Mobile monitoring with wearable photoplethysmographic biosensors. IEEE Eng Med Biol Mag
. 2003; 22 (3): 28–40.
4. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet
. 1986; 1 (8476): 307–10.
5. Brage S, Brage N, Franks PW, et al. Branched equation modeling of simultaneous accelerometry and heart rate monitoring improves estimate of directly measured physical activity energy expenditure. J Appl Physiol
. 2004; 96 (1): 343–51.
6. Brage S, Brage N, Franks PW, et al. Hierarchy of individual calibration levels for heart rate and accelerometry to measure physical activity. J Appl Physiol
. 2007: 103: 682–92.
7. Bravata DM, Smith-Spangler C, Sundaram V, et al. Using pedometers to increase physical activity and improve health. JAMA
. 2007; 298 (19): 2296–304.
8. Crouter SE, Schneider PL, Karabulut M, Bassett DR Jr. Validity of 10 electronic pedometers for measuring steps, distance, and energy cost. Med Sci Sports Exerc
. 2003; 35 (8): 1455–60.
9. Daniels JT. A physiologist’s view of running economy. Med Sci Sports Exerc
. 1985; 17 (3): 332–8.
10. Funk KL, Stevens VJ, Appel LJ. Associations of Internet website use with weight change in a long-term weight loss maintenance program. J Med Internet Res
. 2010; 12 (3): e29.
12. Harris C, Debeliso M, Adams KJ. The effects of running speed on the metabolic and mechanical energy costs of running. J Exerc Physiol
. 2003; 6 (3): 28–37.
13. Headphones: Ownership & Application Study, 2012
. The NPD Group. August 2012. p. 4.
14. Jiang HH, Asada HH, Gibbs P. Active noise cancellation using MEMS accelerometers for motion-tolerant wearable biosensors. In: Conference Proceedings of the IEEE Engineering in Medicine and Biology Society
. 2004. pp. 2157–60.
15. Kong YC, Ming S. Improving energy expenditure estimation by using a triaxial accelerometer. J Appl Physiol
. 1997; 83 (6): 2112–22.
16. Krakow G. Smartphone sales to top 1 billion this year. The Street
17. LeBoeuf SF, Tucker JB, Aumer ME. Light-Guiding Devices and Monitoring Devices Incorporating Same
. U.S. 20100217102. U.S. Patent and Trademark Office. January 21, 2010. p. 1–40.
18. LeBoeuf SF, Tucker JB, Aumer ME. Methods and Apparatus for Assessing Physiological Conditions
. U.S. 20100217099. U.S. Patent and Trademark Office. February 22, 2010. p. 1–40.
19. LeBoeuf SF, Tucker JB, Aumer ME. Noninvasive Physiological Analysis Using Excitation-Sensor Modules and Related Devices and Methods
. U.S. 8251903. U.S. Patent and Trademark Office. October 23, 2008. p. 1–16.
20. LeBoeuf SF, Tucker JB, Aumer ME. Physiological and Environmental Monitoring Systems and Methods
. U.S. 8157730. U.S. Patent and Trademark Office. August 31, 2007. p. 1–32.
21. LeBoeuf SF, Tucker JB, Aumer ME. Telemetric Apparatus for Health and Environmental Monitoring
. U.S. 20080146890. U.S. Patent and Trademark Office. June 12, 2007. p. 1–35.
22. Londeree BR, Thomas TR, Ziogas G, Smith TD, Zhang Q. %V˙O2max
regressions for six modes of exercise. Med Sci Sports Exerc
. 1995; 27 (3): 458–61.
23. Magal M, Eschbach LC, Cain RJ, Bun J. Validity and reliability of an audio headset earbud sensor for heart rate measurements during exercise. presentation abstract. ACSM 59th Annual Meeting
; 2012 May 29–Jun 2: San Francisco (CA). p. 1.
24. Mann T, Tomiyama AJ, Westling E, Lew AM, Samuels B, Chatman J. Medicare’s search for effective obesity treatments: diets are not the answer. American Psychologist
. 2007; 62 (3): 220–33.
25. McArdle WD, Katch FI, Katch VL. Exercise Physiology: Energy, Nutrition and Human Performance
. 5th ed. Baltimore: Williams & Wilkins; 2001. p. 1158.
26. National Health and Nutrition Examination Survey (NHANES). CDC. 2011. p. 242.
27. Relente AR, Sison LG. Characterization and adaptive filtering of motion artifacts in pulse
oximetry using accelerometers. In: Proceedings of 2002 IEEE EMBS Conference, 2002 Magazine
; 2002 Oct 23–26: Philippines. University of the Philippines; 2002. p. 1769–70.
28. Shorten MR, Wootton SA, Williams C. Mechanical energy changes and the oxygen cost of running. Eng Med
. 1981; 10 (4): 213–7.
29. Silcott NA. Evaluation of the Omron HJ-720ITC pedometer under free-living conditions. Med Sci Sports Exerc
. 2011; 43 (9): 1791–7.
30. Slavin MM, Hintermeister RA, Hamill J. A comparison of five mechanical work algorithms for different foot strike patterns and speeds during distance running. In: Hamill J, Derrick TR, Elliot EH, editors. Biomechanics XI. Proceedings of the XIth Symposium of the International Society of Biomechanics in Sports University of Massachusetts Amherst
; 1993 Jun 23–26: Amherst (MA): 1993. pp. 106–9.
31. The U.S. Weight Loss and Diet Control Market (9th edition), Marketdata Enterprises, Inc. 2006. p. 1–393.
32. Van der Walt WH, Wyndham CH. An equation for prediction of energy expenditure of walking and running. J Appl Physiol
. 1973; 34 (5): 559–63.
33. Weir JB. New methods for calculating metabolic rate with special reference to protein metabolism. J Physiol
. 1949; 109 (1–2): 1–9.