Zobrazeno 1 - 10
of 107
pro vyhledávání: '"Norman D Black"'
Autor:
Adele McElroy, Mark P. Donnelly, Suzanne McDonough, Macarena Espinilla, Timothy Patterson, Jane Zheng, Ian Cleland, Paul McCullagh, Norman D. Black, Chris D. Nugent
Publikováno v:
Alzheimer's & Dementia. 14
Autor:
Suzanne McDonough, Federico Cruciani, Timothy Patterson, Adele Boyd, Huiru Zheng, Norman D. Black, Chris D. Nugent, Paul McCullagh, Ian Cleland, Mark P. Donnelly
Publikováno v:
XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016 ISBN: 9783319327013
The number of persons living with chronic conditions such as Chronic Obstructive Pulmonary Disease, Dementia and Stroke is projected to continue to rise in the decades ahead. One emerging approach to reducing the consequent socio-economic burdens is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fe3788a5f956b54c39715ec4b64ddc63
https://doi.org/10.1007/978-3-319-32703-7_175
https://doi.org/10.1007/978-3-319-32703-7_175
Autor:
Yan Huang, Suzanne McDonough, Paul McCullagh, Chris D. Nugent, Norman D. Black, Huiru Zheng, William Burns, Mark A. Tully
Publikováno v:
Health and Technology. 2:249-258
In this paper we present an Orientation Free Adaptive Step Detection (OFASD) algorithm for deployment in a smart phone for the purposes of physical activity monitoring. The OFASD algorithm detects individual steps and measures a user’s step counts
Autor:
Gail Mountain, Huiru Zheng, Richard Davies, Peter Wright, Sue Mawson, Paul McCullagh, Nasrin Nasr, Norman D. Black, C Ecclesone, Leo Galway, Hawley, SJ Parker, Chris D. Nugent
Publikováno v:
Technology and Disability. 24:233-243
The development of technology based interventions for healthcare requires collaborative working between multidis- ciplinary groups of scientists, engineers, designers, healthcare professionals, and of course end users. This necessitates transfer of k
Publikováno v:
Journal of Ambient Intelligence and Humanized Computing. 4:157-168
An activity monitoring and reminder deliveryframework, referred to as iMessenger, is presented. iMessengerincludes five independent modules and adopts alayered structure to assemble each of these modules: contextsensing, context extraction, context m
Autor:
Yan Huang, Norman D. Black, Paul McCullagh, Kevin E. Vowles, Huiru Zheng, Chris D. Nugent, Lance M. McCracken
Publikováno v:
IEEE Transactions on Information Technology in Biomedicine. 15:54-61
Chronic pain is a common long-term condition that affects a person's physical and emotional functioning. Currently, the integrated biopsychosocial approach is the mainstay treatment for people with chronic pain. Self-reporting (the use of questionnai
Autor:
Peter Wright, Huiru Zheng, Richard Davies, Yan Huang, Mark S. Hawley, Paul McCullagh, Sue Mawson, Gail Mountain, Christopher Eccleston, Norman D. Black, Chris D. Nugent, William Burns, Shumei Zhang
Publikováno v:
Journal of Telemedicine and Telecare. 16:224-227
We have developed a personalised self management system to support self management of chronic conditions with support from health-care professionals. Accelerometers are used to measure gross levels of activity, for example walking around the house, a
Publikováno v:
Technology and Health Care. 18:71-87
Medical data include a broad range of data types, including text-based alphanumeric data, recorded physiological signals and medical images. In addition to traditional clinical data, recent progress in medical science and technology has led to the ac
Autor:
Sue Mawson, Norman D. Black, J Hammerton, S Wilson, Nigel Harris, Huiru Zheng, Gail Mountain, Huosheng Hu, Christopher Eccleston, Richard Davies, Thomas Stone, Tricia Ware
Publikováno v:
Journal of Engineering Design. 21:223-236
This paper describes user testing of a technological system which enables stroke survivors to independently undertake rehabilitation exercises at home. The prototype is based on advanced movement sensors which are worn by the user when performing pre
Publikováno v:
Data & Knowledge Engineering. 68:1348-1356
ReliefF has proved to be a successful feature selector but when handling a large dataset, it is computationally expensive. We present an optimization using Supervised Model Construction which improves starter selection. Effectiveness has been evaluat