Using Deep Learning Technology to Realize the Automatic Control Program of Robot Arm Based on Hand Gesture Recognition

Chen, Shang-Liang; Huang, Li-Wu

doi:10.46604/ijeti.2021.7342

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…Chen et al [18] proposed an automated robot arm control program enabling human-robot interaction using the YOLOv4 algorithm. Eight different hand gestures were recorded in a controlled laboratory environment and image features were classified by employing a deep CNN.…”

Section: Related Workmentioning

confidence: 99%

An Integrated Approach towards Efficient Image Classification Using Deep CNN with Transfer Learning and PCA

Sharma

Singh

2022

Adv. technol. innov.

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In image processing, developing efficient, automated, and accurate techniques to classify images with varying intensity level, resolution, aspect ratio, orientation, contrast, sharpness, etc. is a challenging task. This study presents an integrated approach for image classification by employing transfer learning for feature selection and using principal component analysis (PCA) for feature reduction. The PCA algorithm is employed for reducing the dimensionality of the features extracted by the VGG16 model to obtain a handful of features for speeding up image reorganization. For multilayer perceptron classifiers, support vector machine (SVM) and random forest (RF) algorithms are used. The performance of the proposed approach is compared with other classifiers. The experimental results establish the supremacy of the VGG16-PCA-Multilayer perceptron model integrated approach and achieve a reorganization accuracy of 91.145%, 95.0%, 92.33%, and 98.59% on Fashion-MNIST dataset, ORL dataset of faces, corn leaf disease dataset, and rice leaf disease datasets, respectively.

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Section: Related Workmentioning

confidence: 99%

An Integrated Approach towards Efficient Image Classification Using Deep CNN with Transfer Learning and PCA

Sharma

Singh

2022

Adv. technol. innov.

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“…The ultimate purpose of this practical HGR application was to recognize and classify the gestures. Hand recognition was an approach that utilized various concepts and algorithms of numerous methods, such as NNs and image processing, for understanding hand movement [9]. Generally, there were countless applications of HGR.…”

Section: Introductionmentioning

confidence: 99%

Hand Gesture Recognition for Disabled People Using Bayesian Optimization with Transfer Learning

Alrowais¹,

Marzouk²,

Al‐Wesabi³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Sign language recognition can be treated as one of the efficient solutions for disabled people to communicate with others. It helps them to convey the required data by the use of sign language with no issues. The latest developments in computer vision and image processing techniques can be accurately utilized for the sign recognition process by disabled people. American Sign Language (ASL) detection was challenging because of the enhancing intraclass similarity and higher complexity. This article develops a new Bayesian Optimization with Deep Learning-Driven Hand Gesture Recognition Based Sign Language Communication (BODL-HGRSLC) for Disabled People. The BODL-HGRSLC technique aims to recognize the hand gestures for disabled people's communication. The presented BODL-HGRSLC technique integrates the concepts of computer vision (CV) and DL models. In the presented BODL-HGRSLC technique, a deep convolutional neural network-based residual network (ResNet) model is applied for feature extraction. Besides, the presented BODL-HGRSLC model uses Bayesian optimization for the hyperparameter tuning process. At last, a bidirectional gated recurrent unit (BiGRU) model is exploited for the HGR procedure. A wide range of experiments was conducted to demonstrate the enhanced performance of the presented BODL-HGRSLC model. The comprehensive comparison study reported the improvements of the BODL-HGRSLC model over other DL models with maximum accuracy of 99.75%.

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“…In order to solve the above problems, the system we designed uses the improved Canny algorithm [5] to apply to gesture recognition. This method can solve the problem that the threshold needs to be set again when the object to be tested changes.…”

Section: Introductionmentioning

confidence: 99%

Design of An Intelligent Manipulator Controlled by Gestures Based on FPGA

Cong

2023

J. Phys.: Conf. Ser.

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With the development of science and technology, manipulators are increasingly used in industrial production and daily life. The manipulator with traditional control mode has high requirements for operators, complicated operation, and human-computer interaction is inconvenient. In this paper, an intelligent manipulator system based on gesture control is designed. FPGA is used as the control core of the system. We apply the improved Canny algorithm processing, which can automatically recognize gesture information. After judgment, the control signal is sent to the manipulator to control the manipulator to complete the specified action. Through experimental testing, the system can successfully recognize all kinds of gestures, and can well control the manipulator to complete all kinds of actions. The human-computer interaction is more real, which greatly improves the intelligence of the manipulator system and helps to improve the work efficiency.

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Using Deep Learning Technology to Realize the Automatic Control Program of Robot Arm Based on Hand Gesture Recognition

Cited by 7 publications

References 20 publications

An Integrated Approach towards Efficient Image Classification Using Deep CNN with Transfer Learning and PCA

An Integrated Approach towards Efficient Image Classification Using Deep CNN with Transfer Learning and PCA

Hand Gesture Recognition for Disabled People Using Bayesian Optimization with Transfer Learning

Design of An Intelligent Manipulator Controlled by Gestures Based on FPGA

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