Multi-touch gesture recognition of Braille input based on Petri Net and RBF Net

Zhang, Juxiao; Zeng, Xin

doi:10.1007/s11042-021-11156-9

Cited by 6 publications

(4 citation statements)

References 21 publications

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“…This method was dependent upon a conventional neural network (CNN) with long short-term memory networks (LSTM) for capturing the temporal data. In Zhang and Zeng (2022), touch gestures were predictable by a trained radial basis function (RBF) network, but integrated gestures can be demonstrated by Petrinet, which establishes a logic, timing, and spatial relationship model. As a result, the Braille input regarding multi-touch gesture recognition was executed.…”

Section: Related Studiesmentioning

confidence: 99%

Automated Gesture Recognition Using African Vulture Optimization with Deep Learning for Visually Impaired People on Sensory Modality Data

Maashi¹,

Al-Hagery²,

Rizwanullah³

et al. 2023

Journal of Disability Research

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Gesture recognition for visually impaired persons (VIPs) is a useful technology for enhancing their communications and increasing accessibility. It is vital to understand the specific needs and challenges faced by VIPs when planning a gesture recognition model. But, typical gesture recognition methods frequently depend on the visual input (for instance, cameras); it can be vital to discover other sensory modalities for input. The deep learning (DL)-based gesture recognition method is effective for the interaction of VIPs with their devices. It offers a further intuitive and natural way of relating with technology, creating it more available for everybody. Therefore, this study presents an African Vulture Optimization with Deep Learning-based Gesture Recognition for Visually Impaired People on Sensory Modality Data (AVODL-GRSMD) technique. The AVODL-GRSMD technique mainly focuses on the utilization of the DL model with hyperparameter tuning strategy for a productive and accurate gesture detection and classification process. The AVODL-GRSMD technique utilizes the primary data preprocessing stage to normalize the input sensor data. The AVODL-GRSMD technique uses a multi-head attention-based bidirectional gated recurrent unit (MHA-BGRU) method for accurate gesture recognition. Finally, the hyperparameter optimization of the MHA-BGRU method can be performed by the use of African Vulture Optimization with Deep Learning (AVO) approach. A series of simulation analyses were performed to demonstrate the superior performance of the AVODL-GRSMD technique. The experimental values demonstrate the better recognition rate of the AVODL-GRSMD technique compared to that of the state-of-the-art models.

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Section: Related Studiesmentioning

confidence: 99%

Automated Gesture Recognition Using African Vulture Optimization with Deep Learning for Visually Impaired People on Sensory Modality Data

Maashi¹,

Al-Hagery²,

Rizwanullah³

et al. 2023

Journal of Disability Research

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“…It can be seen that, with the increase of center distance, multiquadric is a monotonically increasing function, while Gaussian and inverse multiquadric are monotonically decreasing functions. For monotonically decreasing function, when the variable is close to the function center, the function can obtain a large value; otherwise, its value tends to 0. erefore, monotonically decreasing function has good local characteristics [16]. us, when RBFNN basis function is selected, monotonically decreasing function is usually used.…”

Section: Rbfnn Modelmentioning

confidence: 99%

“…To make the data samples meet the input and output requirements of the Gaussian-RBFNN model, before model prediction, premnmx and postmnmx functions in MAT-LAB7.0 are called to perform normalization and inverse normalization for the input and output models, respectively. e normalized data format of premnmx is shown in formula (16), and the reverse-normalized data format of postmnmx is shown in formula (17) [28,29]:…”

Section: Application Analysis Of the Gaussian-rbfnn Modelmentioning

confidence: 99%

Analysis and Research on Nonlinear Complex Function Approximation Problem Based on Deep Learning

Zhou

2022

Scientific Programming

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Shallow models have limited ability to express high-dimensional nonlinear complex functions. Based on deep learning, a Gaussian radial basis function neural network (RBFNN) is proposed, which is an analysis method of nonlinear complex function approximation based on the Gaussian-RBFNN model. The proposed method can approximate single-variable and binary complex nonlinear functions, and the approximation error is less than 0.1, which can achieve the ideal approximation effect. Finally, the proposed method is applied to analyze the nonlinear complex stock prediction approximation problem, and the effectiveness and practicality of the method are further verified. It can be seen that compared with the BP neural network model, this model performs better in average training time, training standard error, Ratt, RATR, and trmse For RBFNN; the training only needs 50 iterations, while the traditional BP needs 7100 training to gradually converge. In the actual function test, after 275 times of training, the convergence speed gradually tends to be stable, and the predicted value is closer to the actual value. Therefore, the model can be used to analyze practical nonlinear complex function approximation problems.

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“…Therefore, non-linearity is one feature of hand gestures that should be dealt with. It is done through the metadata and content data carried by the images (Zhang and Zeng, 2022). The metadata of hand gesture imageries is utilized for detecting the gestures.…”

Section: Introductionmentioning

confidence: 99%

Design of Information Feedback Firefly Algorithm with a Nested Deep Learning Model for Intelligent Gesture Recognition of Visually Disabled People

Aldehim,

Marzouk,

Al-Hagery

et al. 2023

Journal of Disability Research

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Gesture recognition is a developing topic in current technologies. The focus is to detect human gestures by utilizing mathematical methods for human–computer interaction. Some modes of human–computer interaction are touch screens, keyboard, mouse, etc. All these gadgets have their merits and demerits while implementing versatile hardware in computers. Gesture detection is one of the vital methods to construct user-friendly interfaces. Generally, gestures are created from any bodily state or motion but typically originate from the hand or face. Therefore, this manuscript designs an Information Feedback Firefly Algorithm with Nested Deep Learning (IFBFFA-NDL) model for intelligent gesture recognition of visually disabled people. The presented IFBFFA-NDL technique exploits the concepts of DL with a metaheuristic hyperparameter tuning strategy for the recognition process. To generate a collection of feature vectors, the IFBFFA-NDL technique uses the NASNet model. For optimal hyperparameter selection of the NASNet model, the IFBFFA algorithm is used. To recognize different types of gestures, a nested long short-term memory classification model was used. For exhibiting the improvised gesture detection efficiency of the IFBFFA-NDL technique, a detailed comparative result analysis was conducted and the outcomes highlighted the improved recognition rate of the IFBFFA-NDL technique as 99.73% compared to recent approaches.

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Multi-touch gesture recognition of Braille input based on Petri Net and RBF Net

Cited by 6 publications

References 21 publications

Automated Gesture Recognition Using African Vulture Optimization with Deep Learning for Visually Impaired People on Sensory Modality Data

Automated Gesture Recognition Using African Vulture Optimization with Deep Learning for Visually Impaired People on Sensory Modality Data

Analysis and Research on Nonlinear Complex Function Approximation Problem Based on Deep Learning

Design of Information Feedback Firefly Algorithm with a Nested Deep Learning Model for Intelligent Gesture Recognition of Visually Disabled People

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