Accidental falls and reduced mobility are major risk factors in later life. Changes in a person’s mobility patterns can be related with personal well-being and with the frequency of memory lapses and can be used as risk detectors of incipient neuro-degenerative diseases. Thus, developing technologies for fall detection and indoor localization and novel methods for mobility pattern analysis is of utmost importance in e-health. Choosing the right technology is not only a matter of cost and performance, but also a matter of user acceptability and the perceived ease-of-use by the end user. In this paper, we employ an Analytic Hierarchy Process (AHP) to assess the best fit-to-purpose technology for fall detection and user mobility estimation. Our multi-criteria decision making process is based on the survey results collected from 153 elderly volunteers from 5 EU countries and on 10 emerging e-health technologies for fall detection and indoor mobility pattern estimation. Our analysis points out towards a Bluetooth Low Energy wearable solution as the most suitable solution.
|Journal||EAI Endorsed Transactions on Smart Cities|
|Publication status||Published - Dec 2015|
|Publication type||A1 Journal article-refereed|
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