Disease state fingerprint for fall risk assessment

Heidi Similä; Milla Immonen

doi:10.1109/EMBC.2014.6944297

Disease state fingerprint for fall risk assessment

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-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 language	English
Title of host publication	Proceedings
Subtitle of host publication	36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	3176-3179
ISBN (Electronic)	978-1-4244-7929-0
DOIs	https://doi.org/10.1109/EMBC.2014.6944297
Publication status	Published - 2014
MoE publication type	A4 Article in a conference publication
Event	36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 - Chicago, United States Duration: 26 Aug 2014 → 30 Aug 2014 Conference number: 36

Conference

Conference	36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
Abbreviated title	EMBC 2014
Country/Territory	United States
City	Chicago
Period	26/08/14 → 30/08/14

Keywords

aging
diseases
fingerprint recognition
muscles
risk management
sociology
statistics

Access to Document

10.1109/EMBC.2014.6944297

Cite this

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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",

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language = "English",

pages = "3176--3179",

booktitle = "Proceedings",

publisher = "IEEE Institute of Electrical and Electronic Engineers",

address = "United States",

note = "36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, EMBC 2014 ; Conference date: 26-08-2014 Through 30-08-2014",

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

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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AU - Immonen, Milla

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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.

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