Disease state fingerprint for fall risk assessment

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
CountryUnited States
CityChicago
Period26/08/1430/08/14

Keywords

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

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