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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.

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

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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