A Knowledge-Driven Approach to Predicting Technology Adoption among Persons with Dementia

Timothy Patterson; Sally McClean; Patrick Langdon; Shuai Zhang; CD Nugent; Ian Cleland

doi:10.1109/EMBC.2014.6944978

A Knowledge-Driven Approach to Predicting Technology Adoption among Persons with Dementia

Timothy Patterson
, Sally McClean
, Patrick Langdon
, Shuai Zhang
, CD Nugent
, Ian Cleland

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

247 Downloads (Pure)

Abstract

As the demographics of many countries shift towards an ageing population it is predicted that the prevalence of diseases affecting cognitive capabilities will continually increase. One approach to enabling individuals with cognitive decline to remain in their own homes is through the use of cognitive prosthetics such as reminding technology. However, the benefit of such technologies is intuitively predicated upon their successful adoption and subsequent use. Within this paper we present a knowledge-based feature set which may be utilized to predict technology adoption amongst Persons with Dementia (PwD). The chosen feature set is readily obtainable during a clinical visit, is based upon real data and grounded in established research. We present results demonstrating 86% accuracy in successfully predicting adopters/non-adopters amongst PwD.

Original language	English
Title of host publication	Unknown Host Publication
Publisher	IEEE
Pages	5928-5931
Number of pages	4
DOIs	https://doi.org/10.1109/EMBC.2014.6944978
Publication status	Published (in print/issue) - 30 Aug 2014
Event	36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Sheraton Chicago Hotel and Towers, Chicago, Illinois, USA Duration: 30 Aug 2014 → …

Conference

Conference	36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Period	30/08/14 → …

UN SDGs

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

SDG 3 Good Health and Well-being

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10.1109/EMBC.2014.6944978

A Knowledge-Driven Approach to Predicting Technology Adoption among Persons with DementiaFinal published version, 133 KB

Fusing knowledge and image sensor data for mission-critical control in unmanned aerial vehicles
Patterson, T. (Author), Morrow, P. (Supervisor), McClean, S. (Supervisor) & Parr, G. (Supervisor), Jul 2013
Student thesis: Doctoral Thesis

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Cite this

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title = "A Knowledge-Driven Approach to Predicting Technology Adoption among Persons with Dementia",

abstract = "As the demographics of many countries shift towards an ageing population it is predicted that the prevalence of diseases affecting cognitive capabilities will continually increase. One approach to enabling individuals with cognitive decline to remain in their own homes is through the use of cognitive prosthetics such as reminding technology. However, the benefit of such technologies is intuitively predicated upon their successful adoption and subsequent use. Within this paper we present a knowledge-based feature set which may be utilized to predict technology adoption amongst Persons with Dementia (PwD). The chosen feature set is readily obtainable during a clinical visit, is based upon real data and grounded in established research. We present results demonstrating 86\% accuracy in successfully predicting adopters/non-adopters amongst PwD.",

author = "Timothy Patterson and Sally McClean and Patrick Langdon and Shuai Zhang and CD Nugent and Ian Cleland",

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AU - McClean, Sally

AU - Langdon, Patrick

AU - Zhang, Shuai

AU - Nugent, CD

AU - Cleland, Ian

PY - 2014/8/30

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AB - As the demographics of many countries shift towards an ageing population it is predicted that the prevalence of diseases affecting cognitive capabilities will continually increase. One approach to enabling individuals with cognitive decline to remain in their own homes is through the use of cognitive prosthetics such as reminding technology. However, the benefit of such technologies is intuitively predicated upon their successful adoption and subsequent use. Within this paper we present a knowledge-based feature set which may be utilized to predict technology adoption amongst Persons with Dementia (PwD). The chosen feature set is readily obtainable during a clinical visit, is based upon real data and grounded in established research. We present results demonstrating 86% accuracy in successfully predicting adopters/non-adopters amongst PwD.

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Y2 - 30 August 2014

ER -

A Knowledge-Driven Approach to Predicting Technology Adoption among Persons with Dementia

Abstract

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UN SDGs

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Student theses

Fusing knowledge and image sensor data for mission-critical control in unmanned aerial vehicles

Cite this