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Identifier |
000414781 |
Title |
Feature selection architectures for human activity recognition |
Alternative Title |
Αρχιτεκτονικές επιλογής χαρακτηρισικών για την αναγνώριση ανθρώπινης δραστηριότητας |
Author
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Καραγιαννάκη, Αικατερίνη Ε.
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Thesis advisor
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Τσακαλίδης, Παναγιώτης
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Reviewer
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Μούχταρης, Αθανάσιος
Laerhoven, Kristof Van
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Abstract |
The continuous evolution of sensing technologies during the last decades has resulted
in the production of massive streams of daily generated data for processing and interpretation.
The performance of Human Activity Recognition (HAR) systems is directly affected
by the volume of the produced raw signals due to the continuous need for rapid and accurate
predictions, especially in the case of online/real-time installations. Feature Selection
techniques, as an integral aspect of the Machine Learning/Data Mining pipeline, reduce
the volume of the available raw streams and filter out redundancy, providing the classifiers
with discriminative information about the available data. This thesis explores the effect of
Feature Selection in the HAR domain and it involves three phases.
During the first phase, we perform a benchmark study in order to assess the optimal
parameters, experimental set-up, and suitable algorithms for incorporating Feature
Selection in HAR applications. We evaluate the pipeline for Data Acquisition, Segmentation,
Feature Extraction, and Feature Selection on two different HAR datasets. Our work
demonstrates that the use of short windows during the segmentation stage results in better
classification performance, since the involved data in each window is characterized
by fewer class labels. Concerning Feature Selection, we highlight the effectiveness of
unsupervised graph-based methods.
The second phase of the thesis focuses on the transition into online HAR applications
by incorporating the findings of the benchmark study into an online environment by means
of an Android application. The Data Acquisition process is performed using smaller
batches of data instead of large datasets, simulating a streaming service scenario. We
explore different batch sizes and observe the performance of various Feature Selection
algorithms for various data partitions, with respect to the available activities. We also
perform a qualitative analysis on the selected features and we extract information about
the main modalities that convey dominant features. We evaluate the online performance of
the distinct application components in terms of execution time and we measure the overall
energy requirements on the Android platform. Our experimental results highlight the
contribution of short batches of data to the faster execution of the individual components,
and the efficacy of graph-based techniques to select dominant attributes.
The third and final phase of this thesis explores techniques and architectures for online
feature-level fusion. We expand our Android Feature Selection library and include
methods that merge data based on two approaches, an online Classification scheme and
a Feature Selection architecture, both operating in two stages. The first stage of both
approaches involves the execution of the Machine Learning pipeline on data originating
from different sensor node locations. For the online Classification scheme, the selected
features from each location are merged into a concatenated feature matrix for the further
prediction of the underlying Human Activities on the Android device. The Feature Selection
architecture proceeds into a second layer of feature selection by utilizing the concatenated
matrix of features from different node locations, enriched with inter-location
pairwise correlations. Our results highlight the efficacy of the Feature Selection architecture
to provide high data compression with a minimal computation time overhead.
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Language |
English |
Subject |
Machine learning |
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Wearable devices |
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Μηχανική μάθηση |
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Φορητές συσκευές |
Issue date |
2018-03-23 |
Collection
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School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
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Type of Work--Post-graduate theses
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Permanent Link |
https://elocus.lib.uoc.gr//dlib/4/5/6/metadata-dlib-1520502785-727940-28891.tkl
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Views |
438 |