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
PublisherInstitute of Electrical and Electronic Engineers IEEE
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

Fingerprint

Risk assessment
Visualization
Inspection

Keywords

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

Cite this

Similä, H., & Immonen, M. (2014). Disease state fingerprint for fall risk assessment. In Proceedings: 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014 (pp. 3176-3179). Institute of Electrical and Electronic Engineers IEEE. https://doi.org/10.1109/EMBC.2014.6944297
Similä, Heidi ; Immonen, Milla. / Disease state fingerprint for fall risk assessment. Proceedings: 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014. Institute of Electrical and Electronic Engineers IEEE, 2014. pp. 3176-3179
@inproceedings{c4907ab589184ee2a68d883347a53949,
title = "Disease state fingerprint for fall risk assessment",
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.",
keywords = "aging, diseases, fingerprint recognition, muscles, risk management, sociology, statistics",
author = "Heidi Simil{\"a} and Milla Immonen",
year = "2014",
doi = "10.1109/EMBC.2014.6944297",
language = "English",
pages = "3176--3179",
booktitle = "Proceedings",
publisher = "Institute of Electrical and Electronic Engineers IEEE",
address = "United States",

}

Similä, H & Immonen, M 2014, Disease state fingerprint for fall risk assessment. in Proceedings: 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014. Institute of Electrical and Electronic Engineers IEEE, pp. 3176-3179, 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, Chicago, United States, 26/08/14. https://doi.org/10.1109/EMBC.2014.6944297

Disease state fingerprint for fall risk assessment. / Similä, Heidi; Immonen, Milla.

Proceedings: 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014. Institute of Electrical and Electronic Engineers IEEE, 2014. p. 3176-3179.

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

TY - GEN

T1 - Disease state fingerprint for fall risk assessment

AU - Similä, Heidi

AU - Immonen, Milla

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

KW - aging

KW - diseases

KW - fingerprint recognition

KW - muscles

KW - risk management

KW - sociology

KW - statistics

U2 - 10.1109/EMBC.2014.6944297

DO - 10.1109/EMBC.2014.6944297

M3 - Conference article in proceedings

SP - 3176

EP - 3179

BT - Proceedings

PB - Institute of Electrical and Electronic Engineers IEEE

ER -

Similä H, Immonen M. Disease state fingerprint for fall risk assessment. In Proceedings: 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014. Institute of Electrical and Electronic Engineers IEEE. 2014. p. 3176-3179 https://doi.org/10.1109/EMBC.2014.6944297