Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Hurst, W; El Rhalibi, A; Tully, D
Publisher: Springer Verlag
Languages: English
Types: Unknown
Subjects: QA76, RA

Classified by OpenAIRE into

mesheuropmc: otorhinolaryngologic diseases
On a daily basis, urban residents are unconsciously exposed to hazardous noise levels. This has a detrimental effect on the ear-drum, with symptoms often not apparent till later in life. The impact of harmful noises levels has a damaging impact on wellbeing. It is estimated that 10 million people suffer from damaged hearing in the UK alone, with 6.4million of retirement age or above. With this number expected to increase significantly by 2031, the demand and cost for healthcare providers is expected to intensify. Tinnitus affects about 10 percent of the UK population, with the condition ranging from mild to severe. The effects can have psychological impact on the patient. Often communication becomes difficult, and the sufferer may also be unable to use a hearing aid due to buzzing, ringing or monotonous sounds in the ear. Action on Hearing Loss states that sufferers of hearing related illnesses are more likely to withdraw from social activities. Tinnitus sufferers are known to avoid noisy environments and busy urban areas, as exposure to excessive noise levels exacerbates the symptoms. In this paper, an approach for evaluating and predicting urban noise levels is put forward. The system performs a data classification process to identify and predict harmful noise areas at diverse periods. The goal is to provide Tinnitus sufferers with a real-time tool, which can be used as a guide to find quieter routes to work; identify harmful areas to avoid or predict when noise levels on certain roads will be dangerous to the ear-drum. Our system also performs a visualisation function, which overlays real-time noise levels onto an interactive 3D map.\ud \ud Keywords: Hazardous Noise Levels, Data Classification, Tinnitus, Visualisation, Hearing Loss, Prediction, Real-Time
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • B. Fry, 2008. Visualizing Data, O'Reilly Media; 1 edition (28 Dec. 2007), ISBN-10: 0596514557.
    • W. Hurst., M. Merabti and P. Fergus, 2013. Behavioural Observation for Critical Infrastructure Security Support, 7th European Modelling Symposium on Mathematical Modelling and Computer Simulation, pp 36-41.
    • W. Hurst., M., Merabti, S. Iram and P. Fergus, 2014. Protecting Critical Infrastructures through Behavioural Observation. Inderscience International Journal of Critical Infrastructures, Vol. 10, No. 2, pp 174-192.
    • C. D. Mathers., D. Loncar, 2006. Updated projections of global mortality and burden of disease, 2002-2030: data sources, methods and results. WP on Evidence and Information for Policy World Health Organization.
    • Action on Hearing Loss, [www.actiononhearingloss.org.uk] retrieved May 2015.
    • British Tinnitus Association (BTA) 2015, [www.tinnitus.org.uk/, facts-and-figures]. retrieved May 2015.
    • S. Holmes and N. Padgham, 2009. More than ringing in the ears: a review of tinnitus and its psychosocial impact. Journal of Clinical Nursing, vol 18(21) pp 2927-37.
    • RNID, 2010, What's That Noise? A profile of personal and professional experience of tinnitus in Northern Ireland. RNID, Belfast.
    • J. Brophy, 2013. The Visual Representation of Sound for the Hearing Impaired, Senior Project Electrical Engineering Department California Polytechnic State University San Luis Obispo.
    • D. Tully, A. El Rhalibi, M. Merabti, Y. Shen and C. Carter, 2014, Game Based Decision Support System and Visualisation for Crisis Management and Response. 15th Annual Post-Graduate Symposium on the Convergence of Telecommunications, Networking and Broadcasting pp 283-288.
    • E. Kuncheva, 2004. Combining Pattern Classifiers: Methods and Algorithms.
    • P. Fergus., P. Cheung ., A. Hussain., D. Al-Jumeily., C. Dobbins and S. Iram, 2013. Prediction of Preterm Deliveries from EHG Signals Using Machine Learning, PLoS One, vol. 8, no. 10, p. e77154.
    • R. P. Duin., P. Juszczak., P. Paclik., P. Pakalska., D. De Ridder., D. M. Tax, and S. Verzakov, 2007. A Matlab Toolbox for Pattern Recognition, Version 4. Delft Pattern Recognition Research.
    • F. Lotte, 2009. Study of Electroencephalographic Signal Processing and Classification Techniques towards the use of Brain-Computer Interfaces in Virtual Reality Applications.
    • N. J. Salkind, 2008. Statistics for people who (think they) hate statistics, Third Edition, Sage Publications.
    • S, Figueira., K Nguyen., S Panditrao, 2014. HearThat? - An app for diagnosing hearing loss, Global Humanitarian Technology Conference (GHTC).
    • J. Lang, 1986. Assessment of Noise Impact on the Urban Environment, A Study on Noise Prediction Models, national Institute for Research on Heat and Noise Technology, Austria.
    • E, Lumnitzer., M. Behun., M. Bilova 2011. Implementation of method for dynamic noise visualisation into educational process, 9th International Conference on Emerging eLearning Technologies and Applications (ICETA), pp 17-19.
  • No related research data.
  • No similar publications.

Share - Bookmark

Download from

Cite this article