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Open AccessArticle

FilterNet: A Many-to-Many Deep Learning Architecture for Time Series Classification

Pet Insight Project, Kinship, 1355 Market St #210, San Francisco, CA 94103, USA
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Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2020, 20(9), 2498; https://doi.org/10.3390/s20092498
Received: 21 February 2020 / Revised: 14 April 2020 / Accepted: 26 April 2020 / Published: 28 April 2020
(This article belongs to the Section Intelligent Sensors)
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. View Full-Text
Keywords: activity recognition; time series classification; neural; networks; deep learning; machine learning; CNNs; LSTMs; many-to-many activity recognition; time series classification; neural; networks; deep learning; machine learning; CNNs; LSTMs; many-to-many
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  • Externally hosted supplementary file 1
    Doi: 10.5281/zenodo.3771610
    Link: https://github.com/WhistleLabs/FilterNet
    Description: The source code to reproduce the models, results, and visualizations in this paper are publicly available on GitHub at https://github.com/WhistleLabs/FilterNet
MDPI and ACS Style

Chambers, R.D.; Yoder, N.C. FilterNet: A Many-to-Many Deep Learning Architecture for Time Series Classification. Sensors 2020, 20, 2498.

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