Abstract
| 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 | |
| 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 | United States |
| City | Chicago |
| Period | 26/08/14 → 30/08/14 |
Fingerprint
Keywords
- aging
- diseases
- fingerprint recognition
- muscles
- risk management
- sociology
- statistics
Cite this
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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
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 - IEEE Institute of Electrical and Electronic Engineers
ER -