Technology Adoption and Prediction Tools for Everyday Technologies Aimed at People with Dementia

Priyanka Chaurasia, Sally I McClean, Chris Nugent, Ian Cleland, Shuai Zhang, Mark Donnelly, Scotney Bryan, Chelsea Saunders, Ken Smith, Maria Norton, Tschanz JoAnn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A wide range of assistive technologies have beendeveloped to support the elderly population with the goal ofpromoting independent living. The adoption of these technologybased solutions is, however, critical to their overarching success. Inour previous research we addressed the significance of modellinguser adoption to reminding technologies based on a range ofphysical, environmental and social factors. In our current work webuild upon our initial modeling through considering a wider rangeof computational approaches and identify a reduced set of relevantfeatures that can aid the medical professionals to make an informedchoice of whether to recommend the technology or not. Theadoption models produced were evaluated on a multi-criterionbasis: in terms of prediction performance, robustness and bias inrelation to two types of errors. The effects of data imbalance onprediction performance was also considered. With handling theimbalance in the dataset, a 16 feature-subset was evaluatedconsisting of 173 instances, resulting in the ability to differentiatebetween adopters and non-adopters with an overall accuracy of99.42 %.
LanguageEnglish
Title of host publicationUnknown Host Publication
Number of pages4
Publication statusAccepted/In press - 23 Jun 2016
EventThe 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society -
Duration: 23 Jun 2016 → …

Conference

ConferenceThe 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Period23/06/16 → …

Keywords

  • Technology adoption modelling
  • Dementia
  • Reminding
  • mHealth
  • Assistive technology

Cite this

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title = "Technology Adoption and Prediction Tools for Everyday Technologies Aimed at People with Dementia",
abstract = "A wide range of assistive technologies have beendeveloped to support the elderly population with the goal ofpromoting independent living. The adoption of these technologybased solutions is, however, critical to their overarching success. Inour previous research we addressed the significance of modellinguser adoption to reminding technologies based on a range ofphysical, environmental and social factors. In our current work webuild upon our initial modeling through considering a wider rangeof computational approaches and identify a reduced set of relevantfeatures that can aid the medical professionals to make an informedchoice of whether to recommend the technology or not. Theadoption models produced were evaluated on a multi-criterionbasis: in terms of prediction performance, robustness and bias inrelation to two types of errors. The effects of data imbalance onprediction performance was also considered. With handling theimbalance in the dataset, a 16 feature-subset was evaluatedconsisting of 173 instances, resulting in the ability to differentiatebetween adopters and non-adopters with an overall accuracy of99.42 {\%}.",
keywords = "Technology adoption modelling, Dementia, Reminding, mHealth, Assistive technology",
author = "Priyanka Chaurasia and McClean, {Sally I} and Chris Nugent and Ian Cleland and Shuai Zhang and Mark Donnelly and Scotney Bryan and Chelsea Saunders and Ken Smith and Maria Norton and Tschanz JoAnn",
year = "2016",
month = "6",
day = "23",
language = "English",
isbn = "978-1-4577-0220-4",
booktitle = "Unknown Host Publication",

}

Chaurasia, P, McClean, SI, Nugent, C, Cleland, I, Zhang, S, Donnelly, M, Bryan, S, Saunders, C, Smith, K, Norton, M & JoAnn, T 2016, Technology Adoption and Prediction Tools for Everyday Technologies Aimed at People with Dementia. in Unknown Host Publication. The 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 23/06/16.

Technology Adoption and Prediction Tools for Everyday Technologies Aimed at People with Dementia. / Chaurasia, Priyanka; McClean, Sally I; Nugent, Chris; Cleland, Ian; Zhang, Shuai; Donnelly, Mark; Bryan, Scotney; Saunders, Chelsea; Smith, Ken; Norton, Maria; JoAnn, Tschanz.

Unknown Host Publication. 2016.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Technology Adoption and Prediction Tools for Everyday Technologies Aimed at People with Dementia

AU - Chaurasia, Priyanka

AU - McClean, Sally I

AU - Nugent, Chris

AU - Cleland, Ian

AU - Zhang, Shuai

AU - Donnelly, Mark

AU - Bryan, Scotney

AU - Saunders, Chelsea

AU - Smith, Ken

AU - Norton, Maria

AU - JoAnn, Tschanz

PY - 2016/6/23

Y1 - 2016/6/23

N2 - A wide range of assistive technologies have beendeveloped to support the elderly population with the goal ofpromoting independent living. The adoption of these technologybased solutions is, however, critical to their overarching success. Inour previous research we addressed the significance of modellinguser adoption to reminding technologies based on a range ofphysical, environmental and social factors. In our current work webuild upon our initial modeling through considering a wider rangeof computational approaches and identify a reduced set of relevantfeatures that can aid the medical professionals to make an informedchoice of whether to recommend the technology or not. Theadoption models produced were evaluated on a multi-criterionbasis: in terms of prediction performance, robustness and bias inrelation to two types of errors. The effects of data imbalance onprediction performance was also considered. With handling theimbalance in the dataset, a 16 feature-subset was evaluatedconsisting of 173 instances, resulting in the ability to differentiatebetween adopters and non-adopters with an overall accuracy of99.42 %.

AB - A wide range of assistive technologies have beendeveloped to support the elderly population with the goal ofpromoting independent living. The adoption of these technologybased solutions is, however, critical to their overarching success. Inour previous research we addressed the significance of modellinguser adoption to reminding technologies based on a range ofphysical, environmental and social factors. In our current work webuild upon our initial modeling through considering a wider rangeof computational approaches and identify a reduced set of relevantfeatures that can aid the medical professionals to make an informedchoice of whether to recommend the technology or not. Theadoption models produced were evaluated on a multi-criterionbasis: in terms of prediction performance, robustness and bias inrelation to two types of errors. The effects of data imbalance onprediction performance was also considered. With handling theimbalance in the dataset, a 16 feature-subset was evaluatedconsisting of 173 instances, resulting in the ability to differentiatebetween adopters and non-adopters with an overall accuracy of99.42 %.

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KW - Reminding

KW - mHealth

KW - Assistive technology

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BT - Unknown Host Publication

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