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Prof. Chris Nugent - International Federation on Ageing. Newtownabbey, , IE

Prof. Chris Nugent

Professor of Biomedical Engineering | University of Ulster

Newtownabbey, IRELAND

Chris received a Bachelor of Engineering in Electronic Systems and DPhil in Biomedical Engineering both from the University of Ulster

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Prof Chris Nugent - Ulster University (SERG)

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Biography

Chris received a Bachelor of Engineering in Electronic Systems and DPhil in Biomedical Engineering both from the University of Ulster. He currently holds the position of Professor of Biomedical Engineering at the University.
His research within biomedical engineering addresses the themes of the development and evaluation of Technologies to support ambient assisted living. Specifically, this has involved research in the topics of mobile based reminding solutions, activity recognition and more recently technology adoption modelling. He has published extensively in these areas with the work spanning theoretical, clinical and biomedical engineering domains.

He has been a grant holder of Research Projects funded by National, European and International funding bodies. Amongst these projects he was the Scientific co-ordinator of the European Union MEDICATE consortium, Technical co-ordinator of the European Union CogKnow consortium and Technical co-ordinator of the ESRC New Dynamics of Aging Well Consortium.

At present he is the Acting Director of the Computer Science Research Institute and the Group Leader of the Smart Environments Research Group. He is also the co-PI of the Connected Health Innovation Centre at the University of Ulster.

Industry Expertise (5)

Education/Learning Research Biotechnology Writing and Editing Health and Wellness

Areas of Expertise (5)

Development and Evaluation Technologies Assisted Living Re-enablement Technology Adoption Modelling Health Innovation

Education (2)

University of Ulster: Ph.D., Biomedical Engineering

University of Ulster: B.Eng., Electronic Systems

Affiliations (2)

  • Computer Science Research Institute : Acting Director
  • The Smart Environments Research Group : Group Leader

Languages (1)

  • English

Media Appearances (1)

Positive results for Alzheimer's app

PharmaTimes  online

2015-07-27

"A new app could help prevent the onset of Alzheimer’s disease, according to research unveiled last week.

The Gray Matters app, developed by Ulster University in the UK and Utah State University in the US, encourages users to set lifestyle goals, ranging from exercise and nutrition to stress management and brain stimulation – all of which are known to have an impact on the onset and progression of Alzheimer’s disease.”

Professor Chris Nugent and the app he co-created are highlighted in this article by PharmaTimes.

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Event Appearances (1)

Self-management of health and wellbeing: The role of Smart Environments

Halmstad Colloquium  Sweden

2014-04-07

Featured Articles (5)

A Knowledge-Driven Approach to Activity Recognition in Smart Homes Knowledge and Data Engineering

2012

This paper introduces a knowledge-driven approach to real-time, continuous activity recognition based on multisensor data streams in smart homes. The approach goes beyond the traditional data-centric methods for activity recognition in three ways. First, it makes extensive use of domain knowledge in the life cycle of activity recognition. Second, it uses ontologies for explicit context and activity modeling and representation. Third and finally, it exploits semantic reasoning and classification for activity inferencing, thus enabling both coarse-grained and fine-grained activity recognition. In this paper, we analyze the characteristics of smart homes and Activities of Daily Living (ADL) upon which we built both context and ADL ontologies. We present a generic system architecture for the proposed knowledge-driven approach and describe the underlying ontology-based recognition process. Special emphasis is placed on semantic subsumption reasoning algorithms for activity recognition. The proposed approach has been implemented in a function-rich software system, which was deployed in a smart home research laboratory. We evaluated the proposed approach and the developed system through extensive experiments involving a number of various ADL use scenarios. An average activity recognition rate of 94.44 percent was achieved and the average recognition runtime per recognition operation was measured as 2.5 seconds.

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Sensor-Based Activity Recognition Systems, man, and cybernetics, Part C: Applications and reviews

2012

Research on sensor-based activity recognition has, recently, made significant progress and is attracting growing attention in a number of disciplines and application domains. However, there is a lack of high-level overview on this topic that can inform related communities of the research state of the art. In this paper, we present a comprehensive survey to examine the development and current status of various aspects of sensor-based activity recognition. We first discuss the general rationale and distinctions of vision-based and sensor-based activity recognition. Then, we review the major approaches and methods associated with sensor-based activity monitoring, modeling, and recognition from which strengths and weaknesses of those approaches are highlighted. We make a primary distinction in this paper between data-driven and knowledge-driven approaches, and use this distinction to structure our survey. We also discuss some promising directions for future research.

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Connectivity for Healthcare and Well-Being Management: Examples from Six European Projects International Journal of Environmental Research and Public Health

6 July 2009

Technological advances and societal changes in recent years have contributed to a shift in traditional care models and in the relationship between patients and their doctors/carers, with (in general) an increase in the patient-carer physical distance and corresponding changes in the modes of access to relevant care information by all groups. The objective of this paper is to showcase the research efforts of six projects (that the authors are currently, or have recently been, involved in), CAALYX, eCAALYX, COGKNOW, EasyLine+, I2HOME, and SHARE-it, all funded by the European Commission towards a future where citizens can take an active role into managing their own healthcare. Most importantly, sensitive groups of citizens, such as the elderly, chronically ill and those suffering from various physical and cognitive disabilities, will be able to maintain vital and feature-rich connections with their families, friends and healthcare providers, who can then respond to, and prevent, the development of adverse health conditions in those they care for in a timely manner, wherever the carers and the people cared for happen to be.

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Evidential fusion of sensor data for activity recognition in smart homes Pervasive and Mobile Computing

June 2009

Advances in technology have provided the ability to equip the home environment with a layer of technology to provide a truly ‘Smart Home’. These homes offer improved living conditions and levels of independence for the population who require support with both physical and cognitive functions. At the core of the Smart Home is a collection of sensing technology which is used to monitor the behaviour of the inhabitant and their interactions with the environment. A variety of different sensors measuring light, sound, contact and motion provide sufficient multi-dimensional information about the inhabitant to support the inference of activity determination. A problem which impinges upon the success of any information analysis is the fact that sensors may not always provide reliable information due to either faults, operational tolerance levels or corrupted data. In this paper we address the fusion process of contextual information derived from uncertain sensor data. Based on a series of information handling techniques, most notably the Dempster–Shafer theory of evidence and the Equally Weighted Sum operator, evidential contextual information is represented, analysed and merged to achieve a consensus in automatically inferring activities of daily living for inhabitants in Smart Homes. Within the paper we introduce the framework within which uncertainty can be managed and demonstrate the effects that the number of sensors in conjunction with the reliability level of each sensor can have on the overall decision making process.

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Review of ICT-based services for identified unmet needs in people with dementia Ageing Research Reviews

October 2007

Some of the needs that people with dementia and their informal carers currently perceive as insufficiently met by regular care and support services might be alleviated, or even be met, using modern Information and Communication Technology (ICT). The study described in this paper was designed to provide an insight into the state of the art in ICT solutions that could contribute to meet the most frequently mentioned unmet needs by people with dementia and their informal carers. These needs can be summarized as (1) the need for general and personalized information; (2) the need for support with regard to symptoms of dementia; (3) the need for social contact and company; and (4) the need for health monitoring and perceived safety. Databases that were searched include: PubMed, Cinahl, Psychinfo, Google (Scholar), INSPEC and IEEE. In total 22 websites and 46 publications were included that satisfied the following criteria: the article reports on people with dementia and/or their informal carers and discusses an ICT-device that has been tested within the target group and has proven to be helpful. Within the first need area 18 relevant websites and three studies were included; within the second need area 4 websites and 20 publications were included. Within the third and fourth need area 11 and 12 publications were included respectively. Most articles reported on uncontrolled studies. It is concluded that the informational websites offer helpful information for carers but seem less attuned to the person with dementia and do not offer personalized information. [...]

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