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EAR SIGNED

Audio-based Mobile Health Diagnostics

Total Cost €

0

EC-Contrib. €

0

Partnership

0

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 EAR project word cloud

Explore the words cloud of the EAR project. It provides you a very rough idea of what is the project "EAR" about.

computation    hungry    audio    optimization    advancements    noise    proposes    ethical    grounded    diagnosis    symptoms    away    people    power    patient    sensing    affordable    stage    theory    sounds    computer    medically    associating    indicators    capability    robustness    mobile    tracking    hardware    sampling    demands    potentially    cheap    local    computational    wearable    deployment    powerful    afford    monitoring    resource    rules    quantify    underutilized    link    sensors    limits    wild    additional    arise    onsets    science    human    microphones    fine    fact    near    ad    lives    sort    itself    generally    fit    daily    analytics    reaching    nature    sighs    automatically    populations    time    optimized    maximizing    diagnostic    pilots    offers    ranges    inherent    raised    grail    context    confounding    embed    collection    clinical    source    models    hoc    medical    holy    sparse    voice    sensitive    breathing    body    violates    framework    of    disease    perspective    health    privacy    cloud    data    diagnostics    obvious    deal    delivering    accuracy    threaten   

Project "EAR" data sheet

The following table provides information about the project.

Coordinator
THE CHANCELLOR MASTERS AND SCHOLARSOF THE UNIVERSITY OF CAMBRIDGE 

Organization address
address: TRINITY LANE THE OLD SCHOOLS
city: CAMBRIDGE
postcode: CB2 1TN
website: www.cam.ac.uk

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country United Kingdom [UK]
 Total cost 2˙493˙724 €
 EC max contribution 2˙493˙724 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2018-ADG
 Funding Scheme ERC-ADG
 Starting year 2019
 Duration (year-month-day) from 2019-10-01   to  2024-09-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    THE CHANCELLOR MASTERS AND SCHOLARSOF THE UNIVERSITY OF CAMBRIDGE UK (CAMBRIDGE) coordinator 2˙493˙724.00

Map

 Project objective

Mobile health is becoming the holy grail for affordable medical diagnostics. It has the potential of associating human behaviour with medical symptoms automatically and at early disease stage; it also offers cheap deployment, reaching populations generally not able to afford diagnosis and delivering a level of monitoring so fine which will likely improve diagnostic theory itself. The advancements of technology offer new ranges of sensing and computation capability with the potential of further improving the reach of mobile health. Audio sensing through microphones of mobile devices has recently being recognized as a powerful and yet underutilized source of medical information: sounds from the human body (e.g., sighs, breathing sounds and voice) are indicators of disease or disease onsets. The current pilots, while generally medically grounded, are potentially ad-hoc from the perspective of key areas of computer science; specifically, in their approaches to computational models and how the system resource demands are optimized to fit within the limits of the mobile devices, as well as in terms of robustness needed for tracking people in their daily lives. Audio sensing also comes with challenges which threaten its use in clinical context: its power hungry nature and the fact that audio data is very sensitive and the collection of this sort of data for analytics violates obvious ethical rules. This work proposes models to link sounds to disease diagnosis and to deal with the inherent issues raised by in-the-wild sensing: noise and privacy concerns. We exploit these audio models in wearable systems maximizing the use of local hardware resources with power optimization and accuracy in both near real time and sparse audio sampling. Privacy will arise as a by-product taking away the need of cloud analytics. Moreover, the framework will embed the ability to quantify the diagnostic uncertainty and consider patient context as confounding factors via additional sensors.

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The information about "EAR" are provided by the European Opendata Portal: CORDIS opendata.

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