research-article

Convolutional Neural Networks and Long Short-Term Memory for skeleton-based human activity and hand gesture recognition

Authors:

Juan J. Pantrigo,

Antonio S. Montemayor,

Jos F. VlezAuthors Info & Claims

Pattern Recognition, Volume 76, Issue C

Pages 80 - 94

https://doi.org/10.1016/j.patcog.2017.10.033

Published: 01 April 2018 Publication History

Abstract

Combination of a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) recurrent network for skeleton-based human activity and hand gesture recognition.Two-stage training strategy which firstly focuses on the CNN training and, secondly, adjusts the full method CNN+LSTM.A method for data augmentation in the context of spatiotemporal 3D data sequences.An exhaustive experimental study on publicly available data benchmarks with respect to the state-of-the-art most representative methods.Comparison among different CPU and GPU platforms. In this work, we address human activity and hand gesture recognition problems using 3D data sequences obtained from full-body and hand skeletons, respectively. To this aim, we propose a deep learning-based approach for temporal 3D pose recognition problems based on a combination of a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) recurrent network. We also present a two-stage training strategy which firstly focuses on CNN training and, secondly, adjusts the full method (CNN+LSTM). Experimental testing demonstrated that our training method obtains better results than a single-stage training strategy. Additionally, we propose a data augmentation method that has also been validated experimentally. Finally, we perform an extensive experimental study on publicly available data benchmarks. The results obtained show how the proposed approach reaches state-of-the-art performance when compared to the methods identified in the literature. The best results were obtained for small datasets, where the proposed data augmentation strategy has greater impact.

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Cited By

Shahi SMollyn VTymoszek Park CKang RLiberman ALevy OGong JBedri ALaput G(2024)Vision-Based Hand Gesture Customization from a Single DemonstrationProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676378(1-14)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3654777.3676378
Liu JWang XWang CGao YLiu M(2024)Temporal Decoupling Graph Convolutional Network for Skeleton-Based Gesture RecognitionIEEE Transactions on Multimedia10.1109/TMM.2023.327181126(811-823)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2023.3271811
Zhao DLi HYan S(2024)Spatial–Temporal Synchronous Transformer for Skeleton-Based Hand Gesture RecognitionIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.329508434:3(1403-1412)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1109/TCSVT.2023.3295084
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Convolutional Neural Networks and Long Short-Term Memory for skeleton-based human activity and hand gesture recognition
1. Computing methodologies
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cover image Pattern Recognition

Pattern Recognition Volume 76, Issue C

April 2018

669 pages

ISSN:0031-3203

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Copyright © Elsevier Ltd.

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Elsevier Science Inc.

United States

Publication History

Published: 01 April 2018

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Cited By

Shahi SMollyn VTymoszek Park CKang RLiberman ALevy OGong JBedri ALaput G(2024)Vision-Based Hand Gesture Customization from a Single DemonstrationProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676378(1-14)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3654777.3676378
Liu JWang XWang CGao YLiu M(2024)Temporal Decoupling Graph Convolutional Network for Skeleton-Based Gesture RecognitionIEEE Transactions on Multimedia10.1109/TMM.2023.327181126(811-823)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TMM.2023.3271811
Zhao DLi HYan S(2024)Spatial–Temporal Synchronous Transformer for Skeleton-Based Hand Gesture RecognitionIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.329508434:3(1403-1412)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1109/TCSVT.2023.3295084
Mocaër WAnquetil EKulpa R(2024)Early gesture detection in untrimmed streamsPattern Recognition10.1016/j.patcog.2024.110733156:COnline publication date: 18-Nov-2024
https://dl.acm.org/doi/10.1016/j.patcog.2024.110733
Lotfipoor APatidar SJenkins D(2024)Deep neural network with empirical mode decomposition and Bayesian optimisation for residential load forecastingExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121355237:PAOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121355
Mira AHellwich O(2024)Deep learning models beyond temporal frame-wise features for hand gesture video recognitionThe Journal of Supercomputing10.1007/s11227-024-05910-780:9(12430-12462)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s11227-024-05910-7
He YLiang ZHe SWang YYin M(2024)Viewpoint guided multi-stream neural network for skeleton action recognitionMultimedia Tools and Applications10.1007/s11042-023-15676-483:3(6783-6802)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15676-4
Goyal PRani RSingh K(2024)A multilayered framework for diagnosis and classification of Alzheimer's disease using transfer learned Alexnet and LSTMNeural Computing and Applications10.1007/s00521-023-09301-636:7(3777-3801)Online publication date: 1-Mar-2024
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Tripathi RVerma B(2024)Survey on vision-based dynamic hand gesture recognitionThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-023-03160-x40:9(6171-6199)Online publication date: 1-Sep-2024
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Mahmud HMorshed MHasan M(2024)Quantized depth image and skeleton-based multimodal dynamic hand gesture recognitionThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-022-02762-140:1(11-25)Online publication date: 1-Jan-2024
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