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Open AccessArticle

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

1
Department of Computer Science, TU Dortmund University, 44227 Dortmund, Germany
2
Fraunhofer IML, 44227 Dortmund, Germany
3
Department of Mechanical Engineering, TU Dortmund University, 44227 Dortmund, Germany
*
Author to whom correspondence should be addressed.
Informatics 2018, 5(2), 26; https://doi.org/10.3390/informatics5020026
Received: 7 March 2018 / Revised: 14 May 2018 / Accepted: 22 May 2018 / Published: 25 May 2018
(This article belongs to the Special Issue Sensor-Based Activity Recognition and Interaction)
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). View Full-Text
Keywords: human activity recognition; order picking; convolutional neural networks; multichannel time-series human activity recognition; order picking; convolutional neural networks; multichannel time-series
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Moya Rueda, F.; Grzeszick, R.; Fink, G.A.; Feldhorst, S.; Ten Hompel, M. Convolutional Neural Networks for Human Activity Recognition Using Body-Worn Sensors. Informatics 2018, 5, 26.

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