Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:4407-4410. doi: 10.1109/EMBC.2016.7591704.

Technology adoption and prediction tools for everyday technologies aimed at people with dementia.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

Priyanka Chaurasia, Sally I McClean, Chris D Nugent, Ian Cleland, Shuai Zhang, Mark P Donnelly, Bryan W Scotney, Chelsea Sanders, Ken Smith, Maria C Norton, JoAnn Tschanz

PMID: 28269255 DOI: 10.1109/EMBC.2016.7591704

Abstract

A wide range of assistive technologies have been developed to support the elderly population with the goal of promoting independent living. The adoption of these technology based solutions is, however, critical to their overarching success. In our previous research we addressed the significance of modelling user adoption to reminding technologies based on a range of physical, environmental and social factors. In our current work we build upon our initial modeling through considering a wider range of computational approaches and identify a reduced set of relevant features that can aid the medical professionals to make an informed choice of whether to recommend the technology or not. The adoption models produced were evaluated on a multi-criterion basis: in terms of prediction performance, robustness and bias in relation to two types of errors. The effects of data imbalance on prediction performance was also considered. With handling the imbalance in the dataset, a 16 feature-subset was evaluated consisting of 173 instances, resulting in the ability to differentiate between adopters and non-adopters with an overall accuracy of 99.42 %.

MeSH terms

• Dementia*
• Environment
• Humans
• Independent Living
• Program Evaluation
• Self-Help Devices*

Publication Types

• Journal Article