Cited by 201Context-Aware Activity Recognition SystemsHuman Pose and Action RecognitionTime Series Analysis and Forecasting

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Convolutional Neural Networks for Human Activity Recognition Using Body-Worn Sensors

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Moya Rueda, Fernando, Grzeszick, René, Fink, Gernot A., Feldhorst, Sascha, Ten Hompel, Michael·2018·Informatics

DOI 10.3390/informatics5020026

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Abstract

Human activity recognition (HAR) is a classification task for recognizing human movements. Methods of HAR are of great interest as they have become tools for measuring occurrences and durations of human actions, which are the basis of smart assistive technologies and manual processes analysis. Recently, deep neural networks have been deployed for HAR in the context of activities of daily living using multichannel time-series. These time-series are acquired from body-worn devices, which are composed of different types of sensors. The deep architectures process these measurements for finding basic and complex features in human corporal movements, and for classifying them into a set of human actions. As the devices are worn at different parts of the human body, we propose a novel deep neural network for HAR. This network handles sequence measurements from different body-worn devices separately. An evaluation of the architecture is performed on three datasets, the Oportunity, Pamap2, and an industrial dataset, outperforming the state-of-the-art. In addition, different network configurations will also be evaluated. We find that applying convolutions per sensor channel and per body-worn device improves the capabilities of convolutional neural network (CNNs).

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Authors: Moya Rueda, F., Grzeszick, R., Fink, G. A., Feldhorst, S., & Ten Hompel, M.
Affiliations: Department of Computer Science, TU Dortmund University, 44227 Dortmund, Germany
TU Dortmund University (DE)
Fraunhofer Institute for Material Flow and Logistics (DE)
Fraunhofer IML, 44227 Dortmund, Germany
ORCID: Moya Rueda, Fernando
Grzeszick, René
Fink, Gernot A.
Venue: Informatics
Publisher: MDPI AG
Published: 2018-05-25
Volume: 5
Issue: 2
Pages: 26
ISSN: 2227-9709 (ISSN-L)
DOI: 10.3390/informatics5020026
OpenAlex: W2803675191
Cited by: 196
References: 30
Citations by year: 2026: 12 · 2025: 16 · 2024: 27 · 2023: 30 · 2022: 35 · 2021: 31 · 2020: 29 · 2019: 14 · 2018: 2
FWCI: 9.51
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Informatics (publishedVersion, cc-by)
Technische Universität Dortmund Eldorado (Technische Universität Dortmund) (submittedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion, other-oa)
Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
Technische Universität Dortmund Eldorado (Technische Universität Dortmund) (cc-by)
Funders: Deutsche Forschungsgemeinschaft (Fi799/10-1)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
Keywords: human activity recognition, wearable sensors, convolutional neural networks, activities of daily living, multichannel time series, wearable devices

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Convolutional Neural Networks for Human Activity Recognition Using Body-Worn Sensors

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