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A multi-section hierarchical deep neural network model for time series classification: Applied to wearable sensor-based human activity recognition
Ghorrati, Zahra Esmaeili, Ahmad Matson, Eric T.
Ghorrati, Zahra
Esmaeili, Ahmad
Matson, Eric T.
Authors
Ghorrati, Zahra
Esmaeili, Ahmad
Matson, Eric T.
Esmaeili, Ahmad
Matson, Eric T.
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Issue Date
2024-09-17
Type
Article
Genre
Keywords
Deep learning,Time series classification,Hierarchical classification,Human activity recognition,Wearable sensors
Subjects (LCSH)
Citation
Z. Ghorrati, A. Esmaeili and T. Eric Matson, "A Multi-Section Hierarchical Deep Neural Network Model for Time Series Classification: Applied to Wearable Sensor-Based Human Activity Recognition," in IEEE Access, vol. 12, pp. 137851-137869, 2024, doi: 10.1109/ACCESS.2024.3462515
Abstract
Time Series Classification, is one of the very challenging areas in pattern recognition problems. As the volume of time series data increases, a multitude of TSC algorithms have been developed. Notably, only a limited number of these approaches have ventured into the realm of Deep Neural Networks to tackle this task. This paper presents a new hierarchical deep neural network architecture with a multi-section learning mechanism for the classification of multi-output time series datasets. The hierarchical structure of the proposed approach uses separate segments to detect output classes of different granular levels. The presented model does not suffer from heavy computations during training found in commonly used in other deep learning models such as convolutional neural network. Additionally, our suggested method handles data dimension reduction automatically in the hidden layers of different network sections and does not require separate pre-processing units for this purpose. To demonstrate the effectiveness of the proposed model, we have utilized it in the wearable sensor-based Human Activity Recognition problem and evaluated its performance on several benchmark datasets in TSC and HAR, such as PAMAP2 and Opportunity, with several different configurations. The empirical results on the benchmark datasets show that our proposed model outperforms several other deep learning-based solutions based on measures such as F1 -Score, accuracy, and ROC curve.
Table of Contents
Description
Publisher
IEEE
Journal
IEEE Access
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Digital Collection
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Archival Collection
PubMed ID
ISSN
2169-3536