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EarMetrics®️ Cloud: The untold possibilities of multi-biometric medical data



EarMetrics® is a one-of-a-kind technology. It can be embedded into hearing aids and earphones, aiming to produce medical-grade health data by learning from wearers’ heart rate, heart rate variability, respiration rate and pattern, temperature, oxygen level, and blood pressure. It can also detect gait, activity, and fall detection.


With sensors taking readings from near the ear drum – a central site close to the brain and heart – we believe that the data collected by EarMetrics® will be more accurate than the information gathered by wrist-worn wearables.


With this rich, multi-biometric data presenting so many possibilities, we are creating a responsible cloud architecture – the EarMetrics®️ Cloud – to be the secure store and guardian of the individual's private data. Here’s what sets our EarMetrics® data apart and how cloud access to it could empower future uses for healthcare, clinical research and wellness.


Gathering untapped health insights from the ear


What sets EarMetrics® data apart from that collected by other wearables on the market?


Well, although wrist-worn wearables can provide useable data for wellness monitoring, their accuracy may vary depending on the specific use case and, of course, device quality. They may be suitable for tracking general trends and patterns over time and for providing insights into daily activity and wellbeing metrics. However, their location on the wrist is fundamentally impaired because of its distance from the heart, contact variability, and variations in skin temperature and colour.


The location of the sensor for obtaining accurate data is critical. Studies suggest that people with darker skin could be at risk of worse patient outcomes than lighter-skinned patients because of many pulse oximeters' inherent inaccuracy when detecting blood oxygen levels through black or brown skin. We believe that EarMetrics® technology takes the first racially inclusive central oxygen measurement from the ear, and is not affected by the same variables as wrist-worn devices.


We’re committed to data privacy and user consent


EarSwitch®, founded by former GP Dr Nick Gompertz, is committed to recording data ethically and with full consent from its owners, who will also have full control. Medically robust results have always been at the heart of the organisation, as have user consent and privacy. We are now working with research bodies and hardware organisations to bring this remarkable technology into use, responsibly.


EarMetrics®️ Cloud – the secure store for data collected by our sensors – is a critical part of our offering. With our focus on reliable, medically sound, and usable information, EarMetrics®️ Cloud provides relevant, structured data, ready to use. EarSwitch® is the guardian of this data, and privacy and data security are of the utmost importance to us. The user has complete control over whether they share their data, whether that be to an electronic health record, early warning health alert service, or for anonymous participation in disease research.


The importance of health insights for medical advancements


From remote patient monitoring applications to wellness products and clinical research, the potential for high-quality, multi-biometric data is incredible.


There are already multiple large-scale studies that demonstrate how data collected by wearers is being – and could be – used:


The Health eHeart Study: This digital health study uses wearable devices and mobile apps to collect continuous health data, including heart rate, physical activity, and sleep patterns, from participants. The data is used to explore a wide range of cardiovascular and overall health research questions.


The mPower Study (Parkinson's Disease): Conducted by Sage Bionetworks, the study leveraged data from Apple Watches to monitor motor symptoms in individuals with Parkinson's disease. It aimed to identify patterns and fluctuations in symptoms, which could lead to improved disease management and treatment.


The Digital Biomarker Discovery Pipeline Study: This study, led by the Scripps Research Translational Institute, collected data from a variety of wearable devices, including Fitbit, Apple Watch, and others, to develop digital biomarkers for various health conditions. The goal was to identify patterns in sensor data that could be indicative of specific health outcomes.


It’s clear that the digital provision of health data opens many doors for organisations and individuals. Through our EarMetrics®️ Cloud technology, we hope to learn from and expand upon the current landscape with even more precise health data.


Owing to its ability to record and store synchronised, real-world data in a responsible, clinical-grade cloud architecture, the potential of EarMetrics®️ Cloud is huge – not only for next-level personal health tracking, but trustworthy at-home monitoring, incorporating AI for individual- and population-level insights and trend predictions, and more in-depth and accurate big data research.


Seize your opportunity with EarSwitch®


If you’d like to join us on this exciting journey, one that we hope will contribute to a better equipped and more inclusive health and wellbeing sector, we’d like to hear from you. Apply for your evaluation unit today to explore the many possibilities of our EarMetrics® and EarMetrics®️ Cloud technology.



List of FDA studies

Andrist E, Nuppnau M, Barbaro RP, Valley TS, Sjoding MW. Association of Race With Pulse Oximetry Accuracy in Hospitalized Children. JAMA Netw Open. 2022;5(3):e224584.


Burnett GW, Stannard B, Wax DB, et al. Self-reported Race/Ethnicity and Intraoperative Occult Hypoxemia: A Retrospective Cohort Study. Anesthesiology. 2022;136(5):688-696.


Cabanas AM F-GM, Latorre K, Leon D, Martin-Escudero P. Skin Pigmentation Influence on Pulse Oximetry Accuracy: A Systematic Review and Bibliometric Analysis. Sensors. 2022;22(9).


Fawzy A, Wu TD, Wang K, et al. Racial and Ethnic Discrepancy in Pulse Oximetry and Delayed Identification of Treatment Eligibility Among Patients With COVID-19. JAMA Intern Med. 2022;182(7):730-738.


Henry NR, Hanson AC, Schulte PJ, et al. Disparities in Hypoxemia Detection by Pulse Oximetry Across Self-Identified Racial Groups and Associations With Clinical Outcomes. Crit Care Med.

2022;50(2):204-211.


Valbuena VSM, Barbaro RP, Claar D, et al. Racial Bias in Pulse Oximetry Measurement Among Patients About to Undergo Extracorporeal Membrane Oxygenation in 2019-2020: A Retrospective Cohort Study. Chest. 2022;161(4):971-978.


Vesoulis Z, Tims A, Lodhi H, Lalos N, Whitehead H. Racial discrepancy in pulse oximeter accuracy in preterm infants. J Perinatol. 2022;42(1):79-85.


Wong AI, Charpignon M, Kim H, et al. Analysis of Discrepancies Between Pulse Oximetry and Arterial Oxygen Saturation Measurements by Race and Ethnicity and Association With Organ Dysfunction and Mortality. JAMA Netw Open. 2021;4(11):e2131674.

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