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

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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