<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-07-08T08:44:08Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/6D49324D-72C1-4348-89B0-D6B95819E19F" ns1:id="6D49324D-72C1-4348-89B0-D6B95819E19F"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/E7FC2D36-4721-46F8-83B2-64007B23F17A" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/40D8C1C5-71AD-4557-A47E-4F2089C7EC27" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/40D8C1C5-71AD-4557-A47E-4F2089C7EC27" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2020-12-31T00:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/73906418-9B92-4FCF-9764-197858911259" ns1:rel="FUND" ns1:start="2019-01-01T00:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">104709</ns2:identifier></ns2:identifiers><ns2:title>Diagnosing Sleep Apnoea At Home with a Novel Wearable Technology</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>Collaborative R&amp;D</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>Acurable produces the first truly wearable medical device able to accurately diagnose sleep apnoea in a non-invasive way. Acurable's patented technology is a major engineering innovation and the product of 10 years research at Imperial College London.

Our solution uses a non-invasive wearable sensor (AcuPebble) to monitor acoustic signals of the patient and then applies sophisticated signal treatment algorithms to extract from them the main parameters required for the diagnosis of sleep apnoea. Results from a preliminary clinical study at yield excellent results on the efficacy of a former, suboptimal, version of the device detecting respiratory apnoea events in a clinical setting. The results were published by BMJ Open in 2014\.

This project application covers the technical work required to further enhance the capabilities of the technology within the context of sleep apnea diagnosis. The project is composed of four main work streams: (1) conduct a clinical study to generate the clinical evidence required to demonstrate our technology efficacy on automatic detection of certain physiological biomarkers which are significant for the diagnosis of the condition; (2) build the clinical evidence dossier required for CE marking; (3) Carry out more research into the usability aspects of the system; (4) Develop the first version of a data platform/user interface, which allows the use of AcuPebble, not just for automatic diagnosis of sleep apnoea, but also a signal collection research tools that can be used by data and clinical scientists to carry out research in a wide range of respiratory and cardiac conditions.</ns2:abstractText></ns2:project>