title: Modelling assistive technology adoption for people with dementia.
abstract: Assistive technologies have been identified as a potential solution for the provision of elderly care. Such technologies have in general the capacity to enhance the quality of life and increase the level of independence among their users. Nevertheless, the acceptance of these technologies is crucial to their success. Generally speaking, the elderly are not well-disposed to technologies and have limited experience; these factors contribute towards limiting the widespread acceptance of technology. It is therefore important to evaluate the potential success of technologies prior to their deployment.
  
  
  
  The research described in this paper builds upon our previous work on modelling adoption of assistive technology, in the form of cognitive prosthetics such as reminder apps and aims at identifying a refined sub-set of features which offer improved accuracy in predicting technology adoption. Consequently, in this paper, an adoption model is built using a set of features extracted from a user's background to minimise the likelihood of non-adoption. The work is based on analysis of data from the Cache County Study on Memory and Aging (CCSMA) with 31 features covering a range of age, gender, education and details of health condition. In the process of modelling adoption, feature selection and feature reduction is carried out followed by identifying the best classification models.
  
  
  
  With the reduced set of labelled features the technology adoption model built achieved an average prediction accuracy of 92.48% when tested on 173 participants.
  
  
  
  We conclude that modelling user adoption from a range of parameters such as physical, environmental and social perspectives is beneficial in recommending a technology to a particular user based on their profile.
authors: School of Computing and Mathematics, University of Ulster, Newtownabbey, Co. Antrim, Northern Ireland BT37 0QB, United Kingdom. Electronic address: p.chaurasia@ulster.ac.uk.
authors: Priyanka
authors: P
authors: Chaurasia
authors: School of Computing and Information Engineering, University of Ulster, Coleraine, Northern Ireland BT52 1SA, United Kingdom.
authors: Sally I
authors: SI
authors: McClean
authors: School of Computing and Mathematics, University of Ulster, Newtownabbey, Co. Antrim, Northern Ireland BT37 0QB, United Kingdom.
authors: Chris D
authors: CD
authors: Nugent
authors: School of Computing and Mathematics, University of Ulster, Newtownabbey, Co. Antrim, Northern Ireland BT37 0QB, United Kingdom.
authors: Ian
authors: I
authors: Cleland
authors: School of Computing and Mathematics, University of Ulster, Newtownabbey, Co. Antrim, Northern Ireland BT37 0QB, United Kingdom.
authors: Shuai
authors: S
authors: Zhang
authors: School of Computing and Mathematics, University of Ulster, Newtownabbey, Co. Antrim, Northern Ireland BT37 0QB, United Kingdom.
authors: Mark P
authors: MP
authors: Donnelly
authors: School of Computing and Information Engineering, University of Ulster, Coleraine, Northern Ireland BT52 1SA, United Kingdom.
authors: Bryan W
authors: BW
authors: Scotney
authors: Department of Psychology, Utah State University, Logan, UT 84322-4440, USA.
authors: Chelsea
authors: C
authors: Sanders
authors: Population Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA.
authors: Ken
authors: K
authors: Smith
authors: Department of Family, Consumer, and Human Development, Utah State University, Logan, UT 84322-4440, USA.
authors: Maria C
authors: MC
authors: Norton
authors: Department of Psychology, Utah State University, Logan, UT 84322-4440, USA.
authors: JoAnn
authors: J
authors: Tschanz
mesh_terms: D003198:Computer Simulation; D003704:Dementia / Q000534:rehabilitation*; D004777:Environment; D006801:Humans; D011788:Quality of Life; D012656:Self-Help Devices; D013672:Technology
keywords: Assistive technology; Dementia; Prediction modelling; Technology adoption