Earbud-Based Sensor for the Assessment of Energy Expenditure, HR, and V˙O2max

Abstract

Introduction/Purpose The goal of this program was to determine the feasibility of a novel noninvasive, highly miniaturized optomechanical earbud sensor for accurately estimating total energy expenditure (TEE) and maximum oxygen consumption (VO2max). The optomechanical sensor module, small enough to fit inside commercial audio earbuds, was previously developed to provide a seamless way to measure blood flow information during daily life activities. The sensor module was configured to continuously measure physiological information via photoplethysmography (PPG) and physical activity information via accelerometry. This information was digitized and sent to a microprocessor where digital signal processing (DSP) 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 measurement protocol consisting of treadmill-based cardiopulmonary exercise (CPX) testing to reach VO2max. Benchmark sensors included a 12-lead electrocardiography (ECG) sensor for measuring heart rate, 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 (DUT). Benchmark and DUT data collected from the 14-person training dataset study were integrated into a preconceived statistical model for correlating benchmark data with earbud sensor data. Coefficients were optimized, and the optimized model was validated in the 9-person validation dataset. Results It was observed that the earbud sensor estimated TEE and VO2max with mean ± SD percent estimation errors of −0.7 ± 7.4% and −3.2 ± 7.3% respectively. Conclusion The earbud sensor can accurately estimate TEE and VO2max during CPX testing.

Comparative accuracy of fitness tracking modalities in quantifying energy expenditure

“…[17] However, there is a dearth of evidence in support of this approach to quantifying energy expenditure during exercise, specifically when measured within the ear. Leboeuf et al [10] had participants perform a graded maximal exercise treadmill test, comparing heart rate and caloric expenditure to ECG and indirect calorimetry, respectively. Both variables derived from the earbud sensors were highly correlated (heart rate R 2 ¼ 0.98 kcal, R 2 ¼ 0.86 kcal) with the criterion measures.…”

“…Previous research has shown this technology to be accurate for measuring heart rate and energy expenditure. [10] This can be used to monitor exercise intensity in real time while providing auditory feedback through the earbuds, or provide summative information regarding average heart rate, distance travelled and calories expended at the conclusion of exercise periods.…”

“…This technology has been shown to accurately quantify heart rate and energy expenditure during incremental exercise tests. [10] Given the contrasting nature of measuring energy expenditure and the variety of feedback provided from consumer-grade activity trackers and ear-based sensor technology, understanding the accuracy of these devices is beneficial in aiding individuals in selecting appropriate devices for monitoring exercise output, based on their preferences for wearing a device (i.e. wrist versus ears).…”

A Survey on the Affordances of “Hearables”

“…While hearables have a number of affordances for ubiquitous computing, many of these affordances are particularly relevant for affective computing, in which computers have the ability "to recognize, express, and "have" emotions" [9]. Hearables are particularly useful for recording affective data input, as the ear is a rich source for collecting many types of biological signals pertaining to our emotional states; ear-mounted sensors can potentially monitor cardiovascular, respiratory, perspiration, and brain states [10,11]. This type of data has been utilized in a variety of different applications, including fitness tracking, health monitoring, improved computer interfaces, and various context-aware/"smart" systems.…”

“…Reflective photoplethsmography (PPG) sensors have appeared in a number of different wearable devices, including various health trackers, smartwatches, and hearables [10,28,29], allowing a relatively inexpensive and non-invasive method for obtaining data pertaining to the user's heart rate and heart rate variability. The underlying technology for obtaining this type of heart rate and other blood related activity is remarkably simple, and therefore some wearable devices on the market even contain multiple PPG sensors for redundancy.…”