The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Convolutional Neural Networks for Human Activity Recognition Using Body-Worn Sensors
Open accessAbstract
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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“A majority of the CNNs consist of five to nine layers [23,63,64,113,114,165,166,167,168], usually including two to three convolutional layers, two to three max-pooling layers, followed by one to two fully connected layers before feeding the feature representation into the output layer (softmax layer in most cases).”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [114]
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Dynamic ensemble learning model for Human Activity Recognition using wearable sensors
Alkhafaji, S. K. D., Talib, H. H., Sahi, A., Diykh, M., Ali, M. A., Ratham, H., & Abdulla, S. (2026) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2026.200735
Metadata
- 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
- Venue
- Informatics
- Publisher
- MDPI AG
- Published
- 2018-05-25
- Volume
- 5
- Issue
- 2
- Pages
- 26
- ISSN
- 2227-9709 (ISSN-L)
- 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
- 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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