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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.

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

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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