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Human Activity Recognition by Using Different Deep Learning Approaches for Wearable Sensors

Authors:

Çağatay Berke Erdaş,

Selda GüneyAuthors Info & Claims

Neural Processing Letters, Volume 53, Issue 3

Pages 1795 - 1809

https://doi.org/10.1007/s11063-021-10448-3

Published: 01 June 2021 Publication History

Abstract

With the spread of wearable sensors, the solutions to the task of activity recognition by using the data obtained from the sensors have become widespread. Recognition of activities owing to wearable sensors such as accelerometers, gyroscopes, and magnetometers, etc. has been studied in recent years. Although there are several applications in the literature, differently in this study, deep learning algorithms such as Convolutional Neural Networks, Convolutional LSTM, and 3D Convolutional Neural Networks fed by Convolutional LSTM have been used in human activity recognition task by feeding with data obtained from accelerometer sensor. For this purpose, a frame was formed with raw samples of the same activity which were collected consecutively from the accelerometer sensor. Thus, it is aimed to capture the pattern inherent in the activity and due to preserving the continuous structure of the movement.

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Cited By

Kotte HDaiber FKravcik MDuong-Trung N(2024)FitSight: Tracking and Feedback Engine for Personalized Fitness TrainingProceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3627043.3659547(223-231)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3627043.3659547
Middya AKumar SRoy S(2024)Activity recognition based on smartphone sensor data using shallow and deep learning techniques: A Comparative StudyMultimedia Tools and Applications10.1007/s11042-023-15751-w83:3(9033-9066)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15751-w
Guo AXu YLi G(2023)Exploring the role of multi-scale convolutional operators in behavior sequence recognitionJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-23122045:4(6887-6896)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.3233/JIFS-231220
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Human Activity Recognition by Using Different Deep Learning Approaches for Wearable Sensors

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Information & Contributors

Information

Published In

cover image Neural Processing Letters

Neural Processing Letters Volume 53, Issue 3

Jun 2021

699 pages

ISSN:1370-4621

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 June 2021

Accepted: 02 February 2021

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Cited By

Kotte HDaiber FKravcik MDuong-Trung N(2024)FitSight: Tracking and Feedback Engine for Personalized Fitness TrainingProceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3627043.3659547(223-231)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3627043.3659547
Middya AKumar SRoy S(2024)Activity recognition based on smartphone sensor data using shallow and deep learning techniques: A Comparative StudyMultimedia Tools and Applications10.1007/s11042-023-15751-w83:3(9033-9066)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15751-w
Guo AXu YLi G(2023)Exploring the role of multi-scale convolutional operators in behavior sequence recognitionJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-23122045:4(6887-6896)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.3233/JIFS-231220
Vinod PAnitha A(2023)A Novel Human Activity Recognition Model for Smartphone AuthenticationWireless Personal Communications: An International Journal10.1007/s11277-023-10258-x129:4(2791-2812)Online publication date: 14-Mar-2023
https://dl.acm.org/doi/10.1007/s11277-023-10258-x
Liang JYang CZhong JYe X(2022)BTSwin-Unet: 3D U-shaped Symmetrical Swin Transformer-based Network for Brain Tumor Segmentation with Self-supervised Pre-trainingNeural Processing Letters10.1007/s11063-022-10919-155:4(3695-3713)Online publication date: 17-Jun-2022
https://dl.acm.org/doi/10.1007/s11063-022-10919-1

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