Authors: Moya Rueda, Fernando
Grzeszick, René
Fink, Gernot A.
Feldhorst, Sascha
Ten Hompel, Michael
Title: Convolutional neural networks for human activity recognition using body-worn sensors
Language (ISO): en
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)
Subject Headings: Human activity recognition
Order picking
Convolutional neural networks
Multichannel time-series
Issue Date: 2018-05-25
Rights link:
Appears in Collections:Lehrstuhl für Förder- und Lagerwesen

Files in This Item:
File Description SizeFormat 
informatics-05-00026.pdf975.07 kBAdobe PDFView/Open

This item is protected by original copyright

This item is licensed under a Creative Commons License Creative Commons