Technology adoption and prediction tools for everyday technologies aimed at people with dementia
| Autor: | Chris D. Nugent, Ken R. Smith, Shuai Zhang, Priyanka Chaurasia, Sally McClean, Chelsea Sanders, Bryan Scotney, Ian Cleland, Maria C. Norton, JoAnn T. Tschanz, Mark P. Donnelly |
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| Rok vydání: | 2017 |
| Předmět: |
Engineering
Knowledge management Relation (database) business.industry 020206 networking & telecommunications 02 engineering and technology Environment Self-Help Devices Electronic mail Data modeling 03 medical and health sciences 0302 clinical medicine Work (electrical) Risk analysis (engineering) 0202 electrical engineering electronic engineering information engineering Humans Dementia Independent Living business Robustness (economics) Set (psychology) mHealth 030217 neurology & neurosurgery Independent living Program Evaluation |
| Zdroj: | EMBC |
| ISSN: | 2694-0604 |
| Popis: | 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 %. |
| Databáze: | OpenAIRE |
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