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FilterNet: A Many-to-Many Deep Learning Architecture for Time Series Classification
In this paper, we present and benchmark FilterNet, a flexible deep learning architecture for time series classification tasks, such as activity recognition via multichannel sensor data. It adapts popular convolutional neural network (CNN) and long short-term memory (LSTM) motifs which have excelled...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7249062/ https://www.ncbi.nlm.nih.gov/pubmed/32354082 http://dx.doi.org/10.3390/s20092498 |
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author | Chambers, Robert D. Yoder, Nathanael C. |
author_facet | Chambers, Robert D. Yoder, Nathanael C. |
author_sort | Chambers, Robert D. |
collection | PubMed |
description | In this paper, we present and benchmark FilterNet, a flexible deep learning architecture for time series classification tasks, such as activity recognition via multichannel sensor data. It adapts popular convolutional neural network (CNN) and long short-term memory (LSTM) motifs which have excelled in activity recognition benchmarks, implementing them in a many-to-many architecture to markedly improve frame-by-frame accuracy, event segmentation accuracy, model size, and computational efficiency. We propose several model variants, evaluate them alongside other published models using the Opportunity benchmark dataset, demonstrate the effect of model ensembling and of altering key parameters, and quantify the quality of the models’ segmentation of discrete events. We also offer recommendations for use and suggest potential model extensions. FilterNet advances the state of the art in all measured accuracy and speed metrics when applied to the benchmarked dataset, and it can be extensively customized for other applications. |
format | Online Article Text |
id | pubmed-7249062 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-72490622020-06-10 FilterNet: A Many-to-Many Deep Learning Architecture for Time Series Classification Chambers, Robert D. Yoder, Nathanael C. Sensors (Basel) Article In this paper, we present and benchmark FilterNet, a flexible deep learning architecture for time series classification tasks, such as activity recognition via multichannel sensor data. It adapts popular convolutional neural network (CNN) and long short-term memory (LSTM) motifs which have excelled in activity recognition benchmarks, implementing them in a many-to-many architecture to markedly improve frame-by-frame accuracy, event segmentation accuracy, model size, and computational efficiency. We propose several model variants, evaluate them alongside other published models using the Opportunity benchmark dataset, demonstrate the effect of model ensembling and of altering key parameters, and quantify the quality of the models’ segmentation of discrete events. We also offer recommendations for use and suggest potential model extensions. FilterNet advances the state of the art in all measured accuracy and speed metrics when applied to the benchmarked dataset, and it can be extensively customized for other applications. MDPI 2020-04-28 /pmc/articles/PMC7249062/ /pubmed/32354082 http://dx.doi.org/10.3390/s20092498 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chambers, Robert D. Yoder, Nathanael C. FilterNet: A Many-to-Many Deep Learning Architecture for Time Series Classification |
title | FilterNet: A Many-to-Many Deep Learning Architecture for Time Series Classification |
title_full | FilterNet: A Many-to-Many Deep Learning Architecture for Time Series Classification |
title_fullStr | FilterNet: A Many-to-Many Deep Learning Architecture for Time Series Classification |
title_full_unstemmed | FilterNet: A Many-to-Many Deep Learning Architecture for Time Series Classification |
title_short | FilterNet: A Many-to-Many Deep Learning Architecture for Time Series Classification |
title_sort | filternet: a many-to-many deep learning architecture for time series classification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7249062/ https://www.ncbi.nlm.nih.gov/pubmed/32354082 http://dx.doi.org/10.3390/s20092498 |
work_keys_str_mv | AT chambersrobertd filternetamanytomanydeeplearningarchitecturefortimeseriesclassification AT yodernathanaelc filternetamanytomanydeeplearningarchitecturefortimeseriesclassification |