Hand Gesture Recognition Using Convolutional Neural Network for People Who Have Experienced A Stroke

Alnaim, Norah; Abbod, Maysam F.; Albar, Abdulrahman

doi:10.1109/ismsit.2019.8932739

Cited by 20 publications

(6 citation statements)

References 11 publications

(12 reference statements)

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“…The feature extraction and adapted deep convolutional neural network (ADCNN) utilized for hand classification. The experiment evaluates result for the training data 100% and testing data 99%, with execution time 15,598 s [ 90 ]. While other proposed systems used webcam in order to track hand.…”

Section: Hand Gesture Methodsmentioning

confidence: 99%

Hand Gesture Recognition Based on Computer Vision: A Review of Techniques

2020

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Hand gestures are a form of nonverbal communication that can be used in several fields such as communication between deaf-mute people, robot control, human–computer interaction (HCI), home automation and medical applications. Research papers based on hand gestures have adopted many different techniques, including those based on instrumented sensor technology and computer vision. In other words, the hand sign can be classified under many headings, such as posture and gesture, as well as dynamic and static, or a hybrid of the two. This paper focuses on a review of the literature on hand gesture techniques and introduces their merits and limitations under different circumstances. In addition, it tabulates the performance of these methods, focusing on computer vision techniques that deal with the similarity and difference points, technique of hand segmentation used, classification algorithms and drawbacks, number and types of gestures, dataset used, detection range (distance) and type of camera used. This paper is a thorough general overview of hand gesture methods with a brief discussion of some possible applications.

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Section: Hand Gesture Methodsmentioning

confidence: 99%

Hand Gesture Recognition Based on Computer Vision: A Review of Techniques

2020

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“…It is worth mentioning of convolutional neural networks that take raw images as input, independently extract distinctive visual features, and classify hand gestures (Alnaim et. al., 2019;Chung et.…”

Section: Related Workmentioning

confidence: 99%

Method of Multi-Modal Video Analysis of Hand Movements for Automatic Recognition of Isolated Signs of Russian Sign Language

Axyonov

Ryumin

Kagirov

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. This paper presents a new method for collecting multimodal sign language (SL) databases, which is distinguished by the use of multimodal video data. The paper also proposes a new method of multimodal sign recognition, which is distinguished by the analysis of spatio-temporal visual features of SL units (i.e. lexemes). Generally, gesture recognition is a processing of a video sequence, which helps to extract information on movements of any articulator (a part of the human body) in time and space. With this approach, the recognition accuracy of isolated signs was 88.92%. The proposed method, due to the extraction and analysis of spatio-temporal data, makes it possible to identify more informative features of signs, which leads to an increase in the accuracy of SL recognition.

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“…Стоит отметить использование сверточных нейронных сетей, которые принимают на вход необработанные изображения, самостоятельно извлекают отличительные визуальные признаки и выполняют классификацию жестов рук [26,27].…”

Section: системы основанные на компьютерном зренииunclassified

Approaches to Automatic Gesture Recognition: Hardware and Methods Overview.

Ryumin¹,

Kagirov²

2021

Manned Spaceflight

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In this paper, hardware and software solutions addressed to automatic gesture recognition are considered. Trends in image analysis in the current computer vision-based approaches are analysed. Each of the considered approaches was addressed, in order to reveal their advantages and drawbacks. Research papers on the usability of gesture interfaces were reviewed. It was revealed that sensor-based systems, being quite accurate and demonstrating high speed of recognition, have limited application due to the specificity of devices (gloves, suit) and their relatively narrow distribution. At the same time, computer vision-based approaches can be successfully applied only when problems of occlusions and datasets are solved. The results obtained can be used for designing training systems.

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Hand Gesture Recognition Using Convolutional Neural Network for People Who Have Experienced A Stroke

Cited by 20 publications

References 11 publications

Hand Gesture Recognition Based on Computer Vision: A Review of Techniques

Hand Gesture Recognition Based on Computer Vision: A Review of Techniques

Method of Multi-Modal Video Analysis of Hand Movements for Automatic Recognition of Isolated Signs of Russian Sign Language

Approaches to Automatic Gesture Recognition: Hardware and Methods Overview.

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