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Short-time activity recognition with wearable sensors using convolutional neural network

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Guangjun WangAuthors Info & Claims

VRCAI '16: Proceedings of the 15th ACM SIGGRAPH Conference on Virtual-Reality Continuum and Its Applications in Industry - Volume 1

Pages 413 - 416

https://doi.org/10.1145/3013971.3014016

Published: 03 December 2016 Publication History

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Abstract

Human activity recognition is still a challenging problem in particular environment. In this paper, we propose a novel method based on wearable sensors to effectively recognize the short-time human activity. Our proposed method is based on two stages: First stage, constructing an over-complete pattern library which includes different patterns of short-time human activity. This library is produced by segmenting a long-time activity with sliding window method. Second stage, extracting robust features from an over-completed pattern library and establishing an off-line classification model through convolutional neural network (CNN). Consequently, an outstanding classification result on benchmark database WARD1.0 is successfully achieved based on the previous idea. Experimental results indicate that the proposed method is able to recognize the short-time human activity and at the same time satisfy the requirement of online recognition.

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Cited By

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Hussien MNguyen KCheriet M(2023)A Learning Framework for Bandwidth-Efficient Distributed Inference in Wireless IoTIEEE Sensors Journal10.1109/JSEN.2023.328392323:15(17656-17666)Online publication date: 1-Aug-2023
https://doi.org/10.1109/JSEN.2023.3283923
Han DLee CKang H(2021)Gravity Control-Based Data Augmentation Technique for Improving VR User Activity RecognitionSymmetry10.3390/sym1305084513:5(845)Online publication date: 11-May-2021
https://doi.org/10.3390/sym13050845
Nacer MPrakoonwit SAlarab I(2021)The Combination of AI, Blockchain, and the Internet of Things for Patient Relationship ManagementInternet of Things10.1007/978-3-030-70478-0_3(49-65)Online publication date: 14-Jul-2021
https://doi.org/10.1007/978-3-030-70478-0_3
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Index Terms

Short-time activity recognition with wearable sensors using convolutional neural network
1. Computing methodologies
  1. Machine learning
2. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

VRCAI '16: Proceedings of the 15th ACM SIGGRAPH Conference on Virtual-Reality Continuum and Its Applications in Industry - Volume 1

December 2016

381 pages

ISBN:9781450346924

DOI:10.1145/3013971

Conference Chairs:
Yiyu Cai
Nanyang Technological University, Singapore
,
Daniel Thalmann
École Polytechnique Fédérale de Lausanne, Switzerland and Nanyang Technological University, Singapore

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 December 2016

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VRCAI '16

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SIGGRAPH

VRCAI '16: The 15th International Conference on Virtual-Reality Continuum and its Applications in Industry

December 3 - 4, 2016

Zhuhai, China

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Overall Acceptance Rate 51 of 107 submissions, 48%

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Citations

Cited By

View all

Hussien MNguyen KCheriet M(2023)A Learning Framework for Bandwidth-Efficient Distributed Inference in Wireless IoTIEEE Sensors Journal10.1109/JSEN.2023.328392323:15(17656-17666)Online publication date: 1-Aug-2023
https://doi.org/10.1109/JSEN.2023.3283923
Han DLee CKang H(2021)Gravity Control-Based Data Augmentation Technique for Improving VR User Activity RecognitionSymmetry10.3390/sym1305084513:5(845)Online publication date: 11-May-2021
https://doi.org/10.3390/sym13050845
Nacer MPrakoonwit SAlarab I(2021)The Combination of AI, Blockchain, and the Internet of Things for Patient Relationship ManagementInternet of Things10.1007/978-3-030-70478-0_3(49-65)Online publication date: 14-Jul-2021
https://doi.org/10.1007/978-3-030-70478-0_3
Zhang QLi STang WGuo X(2020)Fast Measurement With Chemical Sensors Based on Sliding Window Sampling and Mixed-Feature ExtractionIEEE Sensors Journal10.1109/JSEN.2020.298503420:15(8740-8745)Online publication date: 1-Aug-2020
https://doi.org/10.1109/JSEN.2020.2985034
Su BWang JLiu SSheng MJiang JXiang K(2019)A CNN-Based Method for Intent Recognition Using Inertial Measurement Units and Intelligent Lower Limb ProsthesisIEEE Transactions on Neural Systems and Rehabilitation Engineering10.1109/TNSRE.2019.290958527:5(1032-1042)Online publication date: May-2019
https://doi.org/10.1109/TNSRE.2019.2909585
Xu WPang YYang YLiu Y(2018)Human Activity Recognition Based On Convolutional Neural Network2018 24th International Conference on Pattern Recognition (ICPR)10.1109/ICPR.2018.8545435(165-170)Online publication date: Aug-2018
https://doi.org/10.1109/ICPR.2018.8545435
Kang JKim JLee SSohn M(2018)Transition activity recognition using fuzzy logic and overlapped sliding window-based convolutional neural networksThe Journal of Supercomputing10.1007/s11227-018-2470-yOnline publication date: 14-Jul-2018
https://doi.org/10.1007/s11227-018-2470-y
Suto JOniga SLung COrha I(2018)Comparison of offline and real-time human activity recognition results using machine learning techniquesNeural Computing and Applications10.1007/s00521-018-3437-xOnline publication date: 20-Mar-2018
https://doi.org/10.1007/s00521-018-3437-x
Suto JOniga SLung COrha I(2017)Recognition rate difference between real-time and offline human activity recognition2017 International Conference on Internet of Things for the Global Community (IoTGC)10.1109/IoTGC.2017.8008967(1-6)Online publication date: Jul-2017
https://doi.org/10.1109/IoTGC.2017.8008967
Suto JOniga S(2017)Efficiency investigation of artificial neural networks in human activity recognitionJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-017-0513-59:4(1049-1060)Online publication date: 26-May-2017
https://doi.org/10.1007/s12652-017-0513-5

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