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Title:
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RECOGNITION OF ARM POSITIONS OF DEMENTIA
PATIENTS VIA SMARTWATCHES USING SUPERVISED
LEARNING |
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Author(s):
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Sergio Staab and Ludger Martin |
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ISBN:
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978-989-8704-38-2 |
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Editors:
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Piet Kommers, Inmaculada Arnedillo Sánchez and Pedro Isaías |
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Year:
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2022 |
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Edition:
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Single |
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Keywords:
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Human Motion Analysis, Machine Learning, Dementia |
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First Page:
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226 |
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Last Page:
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230 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Currently, about 46.8 million people worldwide have dementia. More than 7.7 million new cases occur every year.
Causes and triggers of the disease are currently unknown, and a cure is not available. This makes dementia, along with
cancer, one of the most dangerous diseases in the world. In the field of dementia care, this work attempts to use machine
learning to classify the activities of individuals with dementia in order to track and analyze disease progression and detect
disease-related changes as early as possible. In collaboration with two care communities, exercise data is measured using
the Apple Watch Series 6. Consultation with several care teams that work with dementia patients on a daily basis
revealed that many dementia patients wear watches. In this project data from the aforementioned sensors is sent to the
database at 20 data packets per second via a socket. DecisionTreeClassifier, KNeighborsClassifier, Logistic Regression,
Fast Forest, Support Vector Machine, and Multilayer Perceptron classification algorithms are used to gain knowledge
about locating, providing, and documenting motor skills during the course of dementia. As a first step, arm position
sequences are to be identified, from which different fine-granular activities are to be classified later. |
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