Head mouse control system for people with disabilities

Abiyev, Rahib H.; Arslan, Murat

doi:10.1111/exsy.12398

Cited by 35 publications

(15 citation statements)

References 66 publications

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“…Convolutional neural networks have been employed to overcome big medical challenges like image segmentation [19] and control for people with disabilities [20]. Hussain et al [19] have developed a convolutional neural network designed for the segmentation of the most common brain tumor, i.e., glioma tumor.…”

Section: Related Workmentioning

confidence: 99%

“…This new architecture was capable of achieving the best results among all the proposed and related architectures. Another study by Abiyev and Arslan [20] showed that convolutional neural networks can also be used as supporting elements for people with disabilities. The authors proposed a human-machine interface based on two convolutional neural networks designed for disabled people with spinal cord, to control mouse by eye movements.…”

Section: Related Workmentioning

confidence: 99%

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Application of Deep Learning in Neuroradiology: Brain Haemorrhage Classification Using Transfer Learning

Dawud

Yurtkan

Öztoprak

2019

Computational Intelligence and Neuroscience

104

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In this paper, we address the problem of identifying brain haemorrhage which is considered as a tedious task for radiologists, especially in the early stages of the haemorrhage. The problem is solved using a deep learning approach where a convolutional neural network (CNN), the well-known AlexNet neural network, and also a modified novel version of AlexNet with support vector machine (AlexNet-SVM) classifier are trained to classify the brain computer tomography (CT) images into haemorrhage or nonhaemorrhage images. The aim of employing the deep learning model is to address the primary question in medical image analysis and classification: can a sufficient fine-tuning of a pretrained model (transfer learning) eliminate the need of building a CNN from scratch? Moreover, this study also aims to investigate the advantages of using SVM as a classifier instead of a three-layer neural network. We apply the same classification task to three deep networks; one is created from scratch, another is a pretrained model that was fine-tuned to the brain CT haemorrhage classification task, and our modified novel AlexNet model which uses the SVM classifier. The three networks were trained using the same number of brain CT images available. The experiments show that the transfer of knowledge from natural images to medical images classification is possible. In addition, our results proved that the proposed modified pretrained model “AlexNet-SVM” can outperform a convolutional neural network created from scratch and the original AlexNet in identifying the brain haemorrhage.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Application of Deep Learning in Neuroradiology: Brain Haemorrhage Classification Using Transfer Learning

Dawud

Yurtkan

Öztoprak

2019

Computational Intelligence and Neuroscience

104

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“…e learning of deep networks are basically carried out hierarchically, starting from the lower level to higher, through various layers of the network [25]. Deep learning based on convolution neural networks (CNNs) have been widely used in various areas to solve different engineering problems and showed significant performance in problem solutions [26][27][28][29][30][31][32]. As mentioned, the "in-depth" structures caused an optimization difficulty during training of the networks, i.e., vanishing gradients problem and affected the performance of the network.…”

Section: Residual Learningmentioning

confidence: 99%

Deep Learning Based on Residual Networks for Automatic Sorting of Bananas

Helwan

Ma’aitah²,

Abiyev³

et al. 2021

Journal of Food Quality

Self Cite

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This study presents the design of an intelligent system based on deep learning for grading fruits. For this purpose, the recent residual learning-based network “ResNet-50” is designed to sort out fruits, particularly bananas into healthy or defective classes. The design of the system is implemented by using transfer learning that uses the stored knowledge of the deep structure. Datasets of bananas have been collected for the implementation of the deep structure. The simulation results of the designed system have shown a great generalization capability when tested on test (unseen) banana images and obtained high accuracy of 99%. The simulation results of the designed residual learning-based system are compared with the results of other systems used for grading the bananas. Comparative results indicate the efficiency of the designed system. The developed system can be used in food processing industry, in real-life applications where the accuracy, cost, and speed of the intelligent system will enhance the production rate and allow meeting the demand of consumers. The system can replace or assist human operators who can exert their energy on the selection of fruits.

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“…In this case, the HeadGyro software switch showed slightly higher performance than the HeadCam. Again, in [22], a system is designed to control the movements of the mouse cursor by capturing the head movement. Their study shows that using CNN, the accuracy for head classification is greater than eye blink classification and CNN models perform better than the multilayer perceptron and Histogram of oriented gradients-Support Vector Machine models.…”

Section: A Interaction With Desktop Computermentioning

confidence: 99%

Developing a Novel Hands-Free Interaction Technique Based on Nose and Teeth Movements for Using Mobile Devices

Islam¹,

Aadeeb²,

Khan³

et al. 2021

IEEE Access

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Human-mobile interaction is aimed at facilitating interaction with the smartphone devices. The conventional way to interact with mobile devices is through manual input where most of the applications are made assuming that the end user has full control over their hand movements. However, this assumption excludes people who are unable to use their hands or have suffered limb damage. In this paper, we proposed a nose and teeth based interaction system, which allows the users to control their mobile devices completely hands free. The proposed system uses the front facing camera of the smartphone to track the position of the nose for cursor control on the smartphone screen. The system detects teeth for performing the touch screen events such as tap, scroll, long press, and drag. Viola-Jones algorithm is used to detect the face and teeth based on the Haar features. After detecting the face, the nose position is calculated and tracked continuously using Lucas Kanade's method for optical flow estimation. All the touch screen events have been implemented in the system so that the user can execute all the operations of the smartphone. To evaluate the performance and the effect of (smartphone) device type on the execution time, the proposed system was installed in 3 smartphone devices and 7 trials for each device were performed by 3 different able-bodied elderly persons. The result shows a significant success rate for the detection of nose and teeth, and for the execution of the operations. The execution time of each operation slightly varies by 0.72s on average because of the configuration of the smartphones.INDEX TERMS HCI, human-mobile interaction, gesture operations, disabled user, accessibility, mobile device, smartphone.

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Head mouse control system for people with disabilities

Cited by 35 publications

References 66 publications

Application of Deep Learning in Neuroradiology: Brain Haemorrhage Classification Using Transfer Learning

Application of Deep Learning in Neuroradiology: Brain Haemorrhage Classification Using Transfer Learning

Deep Learning Based on Residual Networks for Automatic Sorting of Bananas

Developing a Novel Hands-Free Interaction Technique Based on Nose and Teeth Movements for Using Mobile Devices

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