Detection of Parkinson's disease symptoms based on wearable sensors
Year: 2019
Type: dissertation
Abstract: A time series is a sequence of points ordered in time.
Time series analysis can often reveal useful patterns for describing certain behavior or for predicting future events.
In this thesis, we experimentally express the relationship between the symptoms severity scores of the patients and their gait signals defined as time series.
We used different deep neural networks for time series classification and
investigated the ability of deep neural networks to automatically extract discriminatory features from raw sensory data.
We show how transferred features from the bottom, middle, or top layer of the neural network for human activity recognition affect the models' performance for detection of the symptoms.
We empirically assess the accuracy of deep neural networks in a practical scenario where we try to automatically predict the patients' symptoms based on their gait signals.
Author: Andrej Petrushevski
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2986659824