Title:
Data fusion of ambient technologies to examine relationships between activity and mild cognitive impairment
Data fusion of ambient technologies to examine relationships between activity and mild cognitive impairment
Author(s)
Zelko, Jacob S.
Advisor(s)
Clifford, Gari D.
Editor(s)
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Abstract
Mild Cognitive Impairment (MCI) is a disorder that affects millions of
elderly individuals across the world. Common symptoms of the condition
are decreases in memory function, impaired motor control which can cause
regularly occurring states of confusion. Arising from this disorder is
the serious concern about how best to support those afflicted by MCI in continuing to live fulfilling, independent lives while protecting them from hazards that may arise from their MCI.
Of particular interest is investigating movement patterns people with
MCI exhibit while pertaining tasks or daily routines. Repetitive movement throughout the same areas of a house -- such as going from a
bathroom, to kitchen, to bedroom in a cyclic fashion -- may indicate increasing severity of MCI. Though there is no known cure or prevention for MCI, identifying if one's condition is getting more severe is imperative to improving quality of life for these individuals.
In this thesis, methods of low-cost location tracking were explored. A
low-cost location tracking system was created and detailed. The system
implemented Bluetooth technologies for identifying individuals. For receivers, devices such as Raspberry Pi
computers were used to record movement patterns of individuals as they
moved around an environment. Simulated scenarios were developed used to create algorithms to determine someone's location in real time.
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Date Issued
2020-05
Extent
Resource Type
Text
Resource Subtype
Undergraduate Thesis