Disease state fingerprint for fall risk assessment

Heidi Similä, Milla Immonen

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    3 Citations (Scopus)

    Abstract

    Fall prevention is an important and complex multifactorial challenge, since one third of people over 65 years old fall at least once every year. A novel application of Disease State Fingerprint (DSF) algorithm is presented for holistic visualization of fall risk factors and identifying persons with falls history or decreased level of physical functioning based on fall risk assessment data. The algorithm is tested with data from 42 older adults, that went through a comprehensive fall risk assessment. Within the study population the Activities-specific Balance Confidence (ABC) scale score, Berg Balance Scale (BBS) score and the number of drugs in use were the three most relevant variables, that differed between the fallers and non-fallers. This study showed that the DSF visualization is beneficial in inspection of an individual's significant fall risk factors, since people have problems in different areas and one single assessment scale is not enough to expose all the people at risk.
    Original languageEnglish
    Title of host publicationProceedings
    Subtitle of host publication36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014
    PublisherIEEE Institute of Electrical and Electronic Engineers
    Pages3176-3179
    ISBN (Electronic)978-1-4244-7929-0
    DOIs
    Publication statusPublished - 2014
    MoE publication typeA4 Article in a conference publication
    Event36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 - Chicago, United States
    Duration: 26 Aug 201430 Aug 2014
    Conference number: 36

    Conference

    Conference36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
    Abbreviated titleEMBC 2014
    Country/TerritoryUnited States
    CityChicago
    Period26/08/1430/08/14

    Keywords

    • aging
    • diseases
    • fingerprint recognition
    • muscles
    • risk management
    • sociology
    • statistics

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