sc 16(3): e2

Research Article

Analytic Hierarchy Process for assessing e-health technologies for elderly indoor mobility analysis

Download410 downloads
  • @ARTICLE{10.4108/eai.14-10-2015.2261667,
        author={Simona Lohan and Oana Cramariuc and Łukasz Malicki and Neja Samar Brenčič and Bogdan Cramariuc},
        title={Analytic Hierarchy Process for assessing e-health technologies for elderly indoor mobility analysis},
        journal={EAI Endorsed Transactions on Smart Cities},
        volume={1},
        number={3},
        publisher={ACM},
        journal_a={SC},
        year={2015},
        month={12},
        keywords={analytic hierarchy process (ahp), elderly e-health care, fall detection, indoor mobility, user surveys},
        doi={10.4108/eai.14-10-2015.2261667}
    }
    
  • Simona Lohan
    Oana Cramariuc
    Łukasz Malicki
    Neja Samar Brenčič
    Bogdan Cramariuc
    Year: 2015
    Analytic Hierarchy Process for assessing e-health technologies for elderly indoor mobility analysis
    SC
    EAI
    DOI: 10.4108/eai.14-10-2015.2261667
Simona Lohan,*, Oana Cramariuc1, Łukasz Malicki2, Neja Samar Brenčič3, Bogdan Cramariuc4
  • 1: TUT/CITST
  • 2: Knowledge Society Association
  • 3: MKS Electr. Syst./ IZRIIS
  • 4: CITST
*Contact email: elena-simona.lohan@tut.fi

Abstract

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.