A Smart Nocturnal Environment Monitoring System for the Hearing Impaired

John Finlay; Stefan Greppmair; Michael Jennings; Matthew Kane; Johannes Leopold; P Catherwood; James Uhomoibhi

A Smart Nocturnal Environment Monitoring System for the Hearing Impaired

John Finlay
, Stefan Greppmair
, Michael Jennings
, Matthew Kane
, Johannes Leopold
, P Catherwood
, James Uhomoibhi

Research output: Contribution to conference › Paper › peer-review

120 Downloads (Pure)

Abstract

This paper presents a novel machine learning prototype to actively assist hearing-impaired people during sleeping hours. The prototype can learn ambient sounds and intelligently decide if they are indicative of dangerous situations such as warning alarms, or if they are merely unimportant background sounds. The application is targeted towards the hearing-impaired who will have removed hearing aids or external components of cochlear implants before sleep. Any sounds of importance can then be translated into vibrations to awaken the sleeping hearing impaired user. The system is suggestive of future smart interactive machines to assist and protect those with either hearing loss or profound deafness.

Original language	English
Pages	1-3
Number of pages	3
Publication status	Published (in print/issue) - 6 Jul 2018
Event	British HCI Conference 2018 - Belfast, Belfast, Northern Ireland Duration: 2 Jul 2018 → 6 Jul 2018

Conference

Conference	British HCI Conference 2018
Abbreviated title	BHCI2018
Country/Territory	Northern Ireland
City	Belfast
Period	2/07/18 → 6/07/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Hearing
Intelligent
Machine learning
Prototypes

Access to Document

HCI 2018 Paper 11 WIPAuthor Accepted Manuscript, 251 KBLicence: Unspecified

Electrocardiographic techniques and methods in the detection of ischaemic heart disease
Jennings, M. (Author), Mc Laughlin, J. (Supervisor), Finlay, D. (Supervisor) & Turner, C. (Supervisor), Dec 2022
Student thesis: Doctoral Thesis

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Cite this

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title = "A Smart Nocturnal Environment Monitoring System for the Hearing Impaired",

abstract = "This paper presents a novel machine learning prototype to actively assist hearing-impaired people during sleeping hours. The prototype can learn ambient sounds and intelligently decide if they are indicative of dangerous situations such as warning alarms, or if they are merely unimportant background sounds. The application is targeted towards the hearing-impaired who will have removed hearing aids or external components of cochlear implants before sleep. Any sounds of importance can then be translated into vibrations to awaken the sleeping hearing impaired user. The system is suggestive of future smart interactive machines to assist and protect those with either hearing loss or profound deafness.",

keywords = "Hearing, Intelligent, Machine learning, Prototypes",

author = "John Finlay and Stefan Greppmair and Michael Jennings and Matthew Kane and Johannes Leopold and P Catherwood and James Uhomoibhi",

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month = jul,

day = "6",

language = "English",

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note = "British HCI Conference 2018, BHCI2018 ; Conference date: 02-07-2018 Through 06-07-2018",

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T1 - A Smart Nocturnal Environment Monitoring System for the Hearing Impaired

AU - Finlay, John

AU - Greppmair, Stefan

AU - Jennings, Michael

AU - Kane, Matthew

AU - Leopold, Johannes

AU - Catherwood, P

AU - Uhomoibhi, James

PY - 2018/7/6

Y1 - 2018/7/6

N2 - This paper presents a novel machine learning prototype to actively assist hearing-impaired people during sleeping hours. The prototype can learn ambient sounds and intelligently decide if they are indicative of dangerous situations such as warning alarms, or if they are merely unimportant background sounds. The application is targeted towards the hearing-impaired who will have removed hearing aids or external components of cochlear implants before sleep. Any sounds of importance can then be translated into vibrations to awaken the sleeping hearing impaired user. The system is suggestive of future smart interactive machines to assist and protect those with either hearing loss or profound deafness.

AB - This paper presents a novel machine learning prototype to actively assist hearing-impaired people during sleeping hours. The prototype can learn ambient sounds and intelligently decide if they are indicative of dangerous situations such as warning alarms, or if they are merely unimportant background sounds. The application is targeted towards the hearing-impaired who will have removed hearing aids or external components of cochlear implants before sleep. Any sounds of importance can then be translated into vibrations to awaken the sleeping hearing impaired user. The system is suggestive of future smart interactive machines to assist and protect those with either hearing loss or profound deafness.

KW - Hearing

KW - Intelligent

KW - Machine learning

KW - Prototypes

M3 - Paper

SP - 1

EP - 3

T2 - British HCI Conference 2018

Y2 - 2 July 2018 through 6 July 2018

ER -

A Smart Nocturnal Environment Monitoring System for the Hearing Impaired

Abstract

Conference

UN SDGs

Keywords

Access to Document

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Student theses

Electrocardiographic techniques and methods in the detection of ischaemic heart disease

Cite this