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Al-Hammadi M, Bencherif MA, Alsulaiman M, Muhammad G, Mekhtiche MA, Abdul W, Alohali YA, Alrayes TS, Mathkour H, Faisal M, Algabri M, Altaheri H, Alfakih T, Ghaleb H. Spatial Attention-Based 3D Graph Convolutional Neural Network for Sign Language Recognition. Sensors (Basel) 2022; 22:s22124558. [PMID: 35746341 PMCID: PMC9227856 DOI: 10.3390/s22124558] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 06/10/2022] [Accepted: 06/13/2022] [Indexed: 02/04/2023]
Abstract
Sign language is the main channel for hearing-impaired people to communicate with others. It is a visual language that conveys highly structured components of manual and non-manual parameters such that it needs a lot of effort to master by hearing people. Sign language recognition aims to facilitate this mastering difficulty and bridge the communication gap between hearing-impaired people and others. This study presents an efficient architecture for sign language recognition based on a convolutional graph neural network (GCN). The presented architecture consists of a few separable 3DGCN layers, which are enhanced by a spatial attention mechanism. The limited number of layers in the proposed architecture enables it to avoid the common over-smoothing problem in deep graph neural networks. Furthermore, the attention mechanism enhances the spatial context representation of the gestures. The proposed architecture is evaluated on different datasets and shows outstanding results.
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Affiliation(s)
- Muneer Al-Hammadi
- Centre of Smart Robotics Research (CS2R), King Saud University, Riyadh 11543, Saudi Arabia; (M.A.-H.); (M.A.); (M.A.M.); (W.A.); (Y.A.A.); (H.M.); (M.F.); (M.A.); (H.A.); (T.A.); (H.G.)
- Department of Civil and Environmental Engineering, Faculty of Engineering, Norwegian University of Science and Technology, Høgskoleringen 1, 7034 Trondheim, Norway
| | - Mohamed A. Bencherif
- Centre of Smart Robotics Research (CS2R), King Saud University, Riyadh 11543, Saudi Arabia; (M.A.-H.); (M.A.); (M.A.M.); (W.A.); (Y.A.A.); (H.M.); (M.F.); (M.A.); (H.A.); (T.A.); (H.G.)
- Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
- Correspondence: (M.A.B.); (G.M.)
| | - Mansour Alsulaiman
- Centre of Smart Robotics Research (CS2R), King Saud University, Riyadh 11543, Saudi Arabia; (M.A.-H.); (M.A.); (M.A.M.); (W.A.); (Y.A.A.); (H.M.); (M.F.); (M.A.); (H.A.); (T.A.); (H.G.)
- Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Ghulam Muhammad
- Centre of Smart Robotics Research (CS2R), King Saud University, Riyadh 11543, Saudi Arabia; (M.A.-H.); (M.A.); (M.A.M.); (W.A.); (Y.A.A.); (H.M.); (M.F.); (M.A.); (H.A.); (T.A.); (H.G.)
- Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
- Correspondence: (M.A.B.); (G.M.)
| | - Mohamed Amine Mekhtiche
- Centre of Smart Robotics Research (CS2R), King Saud University, Riyadh 11543, Saudi Arabia; (M.A.-H.); (M.A.); (M.A.M.); (W.A.); (Y.A.A.); (H.M.); (M.F.); (M.A.); (H.A.); (T.A.); (H.G.)
- Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Wadood Abdul
- Centre of Smart Robotics Research (CS2R), King Saud University, Riyadh 11543, Saudi Arabia; (M.A.-H.); (M.A.); (M.A.M.); (W.A.); (Y.A.A.); (H.M.); (M.F.); (M.A.); (H.A.); (T.A.); (H.G.)
- Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Yousef A. Alohali
- Centre of Smart Robotics Research (CS2R), King Saud University, Riyadh 11543, Saudi Arabia; (M.A.-H.); (M.A.); (M.A.M.); (W.A.); (Y.A.A.); (H.M.); (M.F.); (M.A.); (H.A.); (T.A.); (H.G.)
- Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Tareq S. Alrayes
- Department of Special Education, College of Education, King Saud University, Riyadh 11543, Saudi Arabia;
| | - Hassan Mathkour
- Centre of Smart Robotics Research (CS2R), King Saud University, Riyadh 11543, Saudi Arabia; (M.A.-H.); (M.A.); (M.A.M.); (W.A.); (Y.A.A.); (H.M.); (M.F.); (M.A.); (H.A.); (T.A.); (H.G.)
- Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Mohammed Faisal
- Centre of Smart Robotics Research (CS2R), King Saud University, Riyadh 11543, Saudi Arabia; (M.A.-H.); (M.A.); (M.A.M.); (W.A.); (Y.A.A.); (H.M.); (M.F.); (M.A.); (H.A.); (T.A.); (H.G.)
- Center of AI & Robotics, Kuwait College of Science and Technology (KCST), Kuwait City 35004, Kuwait
| | - Mohammed Algabri
- Centre of Smart Robotics Research (CS2R), King Saud University, Riyadh 11543, Saudi Arabia; (M.A.-H.); (M.A.); (M.A.M.); (W.A.); (Y.A.A.); (H.M.); (M.F.); (M.A.); (H.A.); (T.A.); (H.G.)
- Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Hamdi Altaheri
- Centre of Smart Robotics Research (CS2R), King Saud University, Riyadh 11543, Saudi Arabia; (M.A.-H.); (M.A.); (M.A.M.); (W.A.); (Y.A.A.); (H.M.); (M.F.); (M.A.); (H.A.); (T.A.); (H.G.)
- Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Taha Alfakih
- Centre of Smart Robotics Research (CS2R), King Saud University, Riyadh 11543, Saudi Arabia; (M.A.-H.); (M.A.); (M.A.M.); (W.A.); (Y.A.A.); (H.M.); (M.F.); (M.A.); (H.A.); (T.A.); (H.G.)
| | - Hamid Ghaleb
- Centre of Smart Robotics Research (CS2R), King Saud University, Riyadh 11543, Saudi Arabia; (M.A.-H.); (M.A.); (M.A.M.); (W.A.); (Y.A.A.); (H.M.); (M.F.); (M.A.); (H.A.); (T.A.); (H.G.)
- Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
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Altuwaijri GA, Muhammad G, Altaheri H, Alsulaiman M. A Multi-Branch Convolutional Neural Network with Squeeze-and-Excitation Attention Blocks for EEG-Based Motor Imagery Signals Classification. Diagnostics (Basel) 2022; 12:diagnostics12040995. [PMID: 35454043 PMCID: PMC9032940 DOI: 10.3390/diagnostics12040995] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 04/10/2022] [Accepted: 04/12/2022] [Indexed: 02/04/2023] Open
Abstract
Electroencephalography-based motor imagery (EEG-MI) classification is a critical component of the brain-computer interface (BCI), which enables people with physical limitations to communicate with the outside world via assistive technology. Regrettably, EEG decoding is challenging because of the complexity, dynamic nature, and low signal-to-noise ratio of the EEG signal. Developing an end-to-end architecture capable of correctly extracting EEG data’s high-level features remains a difficulty. This study introduces a new model for decoding MI known as a Multi-Branch EEGNet with squeeze-and-excitation blocks (MBEEGSE). By clearly specifying channel interdependencies, a multi-branch CNN model with attention blocks is employed to adaptively change channel-wise feature responses. When compared to existing state-of-the-art EEG motor imagery classification models, the suggested model achieves good accuracy (82.87%) with reduced parameters in the BCI-IV2a motor imagery dataset and (96.15%) in the high gamma dataset.
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Affiliation(s)
- Ghadir Ali Altuwaijri
- Department of Computer Engineering, College of Computer and Information Sciences (CCIS), King Saud University, Riyadh 11543, Saudi Arabia; (G.A.A.); (H.A.); (M.A.)
| | - Ghulam Muhammad
- Department of Computer Engineering, College of Computer and Information Sciences (CCIS), King Saud University, Riyadh 11543, Saudi Arabia; (G.A.A.); (H.A.); (M.A.)
- Centre of Smart Robotics Research (CS2R), King Saud University, Riyadh 11543, Saudi Arabia
- Correspondence:
| | - Hamdi Altaheri
- Department of Computer Engineering, College of Computer and Information Sciences (CCIS), King Saud University, Riyadh 11543, Saudi Arabia; (G.A.A.); (H.A.); (M.A.)
- Centre of Smart Robotics Research (CS2R), King Saud University, Riyadh 11543, Saudi Arabia
| | - Mansour Alsulaiman
- Department of Computer Engineering, College of Computer and Information Sciences (CCIS), King Saud University, Riyadh 11543, Saudi Arabia; (G.A.A.); (H.A.); (M.A.)
- Centre of Smart Robotics Research (CS2R), King Saud University, Riyadh 11543, Saudi Arabia
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Altaheri H, Alsulaiman M, Muhammad G, Amin SU, Bencherif M, Mekhtiche M. Date fruit dataset for intelligent harvesting. Data Brief 2019; 26:104514. [PMID: 31667277 PMCID: PMC6811983 DOI: 10.1016/j.dib.2019.104514] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Revised: 08/28/2019] [Accepted: 09/06/2019] [Indexed: 10/31/2022] Open
Abstract
The date palm is one of the most valuable fruit trees in the world. Most methods used for date fruit inspection, harvesting, grading, and classification are manual, which makes them ineffective in terms of both time and economy. Research on automated date fruit harvesting is limited as there is no public dataset for date fruits to aid in this. In this work, we present a comprehensive dataset for date fruits that can be used by the research community for multiple tasks including automated harvesting, visual yield estimation, and classification tasks. The dataset contains images of date fruit bunches of different date varieties, captured at different pre-maturity and maturity stages. These images cover multiple sets of variations such as multi-scale images, variable illumination, and different bagging states. We also marked date bunches for selected palms and measured the weights of the bunches, captured their images on a graph paper, and recorded 360° video of the palms. This dataset can help in advancing research and automating date palm agricultural applications, including robotic harvesting, fruit detection and classification, maturity analysis, and weight/yield estimation. The dataset is freely and publicly available for the research community in the IEEE DataPort repository [1] (https://doi.org/10.21227/x46j-sk98).
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Affiliation(s)
- Hamdi Altaheri
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.,Center of Smart Robotics Research, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Mansour Alsulaiman
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.,Center of Smart Robotics Research, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Ghulam Muhammad
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.,Center of Smart Robotics Research, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Syed Umar Amin
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.,Center of Smart Robotics Research, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Mohamed Bencherif
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.,Center of Smart Robotics Research, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Mohamed Mekhtiche
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.,Center of Smart Robotics Research, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
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