2026-04-21T20:06:23Zhttps://ebiltegia.mondragon.edu/oai/requestoai:ebiltegia.mondragon.edu:20.500.11984/11272024-04-15T15:46:40Zcom_20.500.11984_460col_20.500.11984_469
eBiltegia
advisor
Aztiria Goenaga, Asier
advisor
Basarab, Adrian
author
Alberdi Aramendi, Ane
2018-11-12T08:51:12Z
2018-11-12T08:51:12Z
2017
https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=148152
https://hdl.handle.net/20.500.11984/1127
There are many disorders that directly affect people’s behaviour. The people that are suffering from such a disorder are not aware of their situation, and too often the disorders are identified by relatives or co-workers because they notice behavioural shifts. However, when these changes become noticeable, it is often too late and irreversible damages have already been produced. Early detection is the key to prevent severe health-related damages and healthcare costs, as well as to improve people’s quality of life.
Nowadays, in full swing of ubiquitous computing paradigm, users’ behaviour patterns can be unobtrusively monitored by means of interactions with many electronic devices. The application of this technology for the problem at hand would lead to the development of systems that are able to monitor disorders’ onset and progress in an ubiquitous and unobtrusive way, thus enabling their early detection. Some attempts for the detection of specific disorders based on these technologies have been proposed, but a global methodology that could be useful for the early detection of a wide range of disorders is still missing.
This thesis aims to fill that gap by presenting as main contribution a global screening methodology for the early detection of disorders based on unobtrusive monitoring of physiological and behavioural data. The proposed methodology is the result of a cross-case analysis between two individual validation scenarios: stress in the workplace and Alzheimer’s Disease (AD) at home, from which conclusions that contribute to each of the two research fields have been drawn. The analysis of similarities and
differences between the two case studies has led to a complete and generalized definition of the steps to be taken for the detection of a new disorder based on ubiquitous computing.
eng
© 2017 Ane Alberdi Aramendi
Análisis de datos
Teoría y procesos de decisión
Inteligencia artificial
Bioestádistica
ODS 3 Salud y bienestar
ODS 11 Ciudades y comunidades sostenibles
Early diagnosis of disorders based on behavioural shifts and biomedical signals
http://purl.org/coar/resource_type/c_db06
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URL
http://ebiltegia.mondragon.edu/xmlui/bitstream/20.500.11984/1127/1/Tesis%20Alberdi%20Aramendi%20Ane.pdf
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Tesis Alberdi Aramendi Ane.pdf
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Tesis Alberdi Aramendi Ane.pdf.txt