Abstract
Falls pose a serious threat to older people, since they
may lead to severe injuries,
reduced quality of life and increased health care costs.
Every third person over 65 years old falls at least once
each year, and the number of falls increases with age and
frailty level. Falls are multifactorial by nature and a
person can have several risk factors contributing to a
fall. A variety of assessment scales have been developed
for assessing fall risk factors and estimating the
probability of future falls. These are typically
administrated by a health care professional. However,
selection of an assessment scale with high enough
sensitivity and specificity and reasonable administration
time can be difficult. The goal of this thesis was to
develop new methods for fall risk assessment
utilizing accelerometry-based movement sensing, which
enables objective detection and assessment of a person's
balance deficits. The first objective was to investigate
the perceptions of prospective end-users of new
technologies via focus group interviews. The analysis
showed that familiarity, prior experience and
self-efficacy presumably affect the acceptance of new
solutions. The second objective was to investigate how an
individual's fall risk is manifested through different
assessment scales. The Disease State Fingerprint
visualization method was examined for its potential in
comparing different fall risk assessment scales. It was
found useful in discovering the most relevant assessment
scales for separating fallers from non-fallers in the
study population, and for presenting
how the overall fall risk of an individual is
constituted. The third objective was to
study how body-worn accelerometry could be utilized in
the assessment of individual fall risk. For the third
objective, three data sets were collected from a total of
111 subjects. The results showed that features derived
from the body-worn accelerometer signals could be used
for assessment of a person's balance. Furthermore, they
seem to be able detect balance deficits even earlier than
the traditionally used clinical assessment scales. The
results provide a basis for studies validating these
methods and further transferring them into practice.
Original language | English |
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Qualification | Doctor Degree |
Awarding Institution |
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Supervisors/Advisors |
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Award date | 17 Nov 2017 |
Place of Publication | Espoo |
Publisher | |
Print ISBNs | 978-951-38-8573-1 |
Electronic ISBNs | 978-951-38-8572-4 |
Publication status | Published - 2017 |
MoE publication type | G5 Doctoral dissertation (article) |
Keywords
- balance
- wearable sensors
- machine learning
- gait analysis