Recognition of user-dependent and independent static hand gestures: Application to sign language

Sadeddine, Khadidja; Chelali, Fatma Zohra; Djeradi, Rachida; Djéradi, Amar; Benabderrahmane, Sidahmed

doi:10.1016/j.jvcir.2021.103193

Cited by 24 publications

(7 citation statements)

References 34 publications

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“…It is clear from Table 10 , when comparing recognition rates obtained for each architecture, that our proposed method applied on Triesch’s dataset, achieved a better score varying from 97.5 to 99.17%, which is motivating and encouraging compared to those obtained in [ 24 , 35 ], we also notice that better results were achieved in [ 54 ] for the black background by increasing the training percentage to 75%.…”

Section: Simulation Resultssupporting

confidence: 62%

“…For Arabic Sign language dataset, the combined DT-CWT + HOG achieved a 94.89% recognition rate which is better than the results cited in [ 3 , 9 , 53 ]. However, better a recognition rate of 95.83% was achieved in [ 54 ] due to employing a training percentage of 75%. Besides, a score of 100% was obtained for the dynamic dataset using the combined descriptor DT-CWT + HOG and the Random Forest classifier, which is better than the results obtained in [ 2 , 41 ].…”

Section: Simulation Resultsmentioning

confidence: 99%

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New combined DT-CWT and HOG descriptor for static and dynamic hand gesture recognition

Agab

Chelali

2023

Multimed Tools Appl

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In recent years, researchers have been focusing on developing Human-Computer Interfaces that are fast, intuitive, and allow direct interaction with the computing environment. One of the most natural ways of communication is hand gestures. In this context, many systems were developed to recognize hand gestures using numerous vision-based techniques, these systems are highly affected by acquisition constraints, such as resolution, noise, lighting condition, hand shape, and pose. To enhance the performance under such constraints, we propose a static and dynamic hand gesture recognition system, which utilizes the Dual-Tree Complex Wavelet Transform to produce an approximation image characterized by less noise and redundancy. Subsequently, the Histogram of Oriented Gradients is applied to the resulting image to extract relevant information and produce a compact features vector. For classification, we compare the performance of three Artificial Neural Networks, namely, MLP, PNN, and RBNN. Random Decision Forest and SVM classifiers are also used to ameliorate the efficiency of our system. Experimental evaluation is performed on four datasets composed of alphabet signs and dynamic gestures. The obtained results demonstrate the efficiency of the combined features, for which the achieved recognition rates were comparable to the state-of-the-art.

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Section: Simulation Resultssupporting

confidence: 62%

Section: Simulation Resultsmentioning

confidence: 99%

New combined DT-CWT and HOG descriptor for static and dynamic hand gesture recognition

Agab

Chelali

2023

Multimed Tools Appl

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“…Thus, the scholar Orlova, in her work, notes "... almost complete absence of tools that facilitate the barrier-free transmission of information, the fragmentation of their practical application in modern Russia" (Orlova, 2016). Basically, the tasks of such research are the analysis of information transformation methods, the development of concepts for information support of appropriate communications, the creation of models, methods of transformation and visualization of information and algorithms for information support of communication and their software implementation for each category of people, depending on their health capabilities (Skorobogatova 2012;4th International Workshop on "Photogrammetric and Computer Vision Techniques for Video Surveillance, Biometrics and Biomedicine, 2021; Lagha & Othman, 2019;Prikhodko et al, 2020;Sadeddine et al, 2021). Within the framework of this research, the results of such studies are of interest from the point of view of information visualization, particularly the accuracy of static and dynamic gestures, the position of the human hand, the features of a person's lip contour, etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Information System for Learning Control in Teaching Russian Sign Language: Process and Data Modeling

Logachev¹,

Chernova²,

Laamarti³

et al. 2022

INT J INSTRUCTION

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The article describes project of the information system to control the teaching of Russian sign language. The aim of the conducted study is to consolidate existing algorithms and software tools to ensure the integrity, and objectivity in the implementation of professional training of specialists in the field of Russian sign language. The authors conducted a study of specialized sources in order to highlight the features of the subject area for the implementation of design processes. With the employment of structural analysis methods, a formal data model is obtained that describes the concept of a database that provides data storage and processing necessary for the full-scale operation of the information system. Using a combination of methods of structural analysis and object-oriented design, a model of the process of user interaction with the designed information system is obtained. The advantage of such a model is the selection of data objects necessary for the implementation of each stage of the process, as well as the objects obtained and available after their execution. All users of the system are classified according to the principle of data availability. The results obtained at the project stage in the future are the basis for the development of the corresponding software product.

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“…So, proper calibration is required every time a new user is introduced. This situation is not effective since the solution is partially general and self calibration takes time for the process [46]. \ The naïve Bayes method is implemented to improve the accuracy of the system.…”

Section: B Hand Gesture Recognitionmentioning

confidence: 99%

Electric Wheelchair Control Using Wrist Rotation Based on Analysis of Muscle Fatigue

et al. 2022

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The wheelchair is one of the most common assistive technologies for people with motor impairment, due to its environmentally friendly features in terms of mobility and comfort. However, the operational method for conventional wheelchair is still inconvenience for people with finger problems. Therefore, in the project , a novel control method to operate an electric wheelchair, using hand gestures ( wrist rotation) is developed. Sixty-five (65) participants were involved in this study. Five hand gestures were considered and studied for forward, backward, right, left, and stop maneuvers while considering human ergonomics factor. In this study, the stop maneuver was determined based on the most comfortable hand position to mitigate a fatigue experience, with two gesture classifying methods further investigated. The first method based on threshold has a promising accuracy of 96%, and 91% precision. This method however requires a calibration every time a new user is introduced. The second method, a Naïve Bayes approach, was observed to solve the problem, as it has about 99% of both the accuracy and the precision. The evaluation of this method was then conducted with six participants that operated the wheelchair to follow the trajectories from start to end. The results showed that the participants comfortably controlled the developed wheelchair system to the goal without any collision. Results from experiments indicate that the proposed approach has high accuracy and the potential to solve the problem related to finger dependencies and hand fatigue.

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Recognition of user-dependent and independent static hand gestures: Application to sign language

Cited by 24 publications

References 34 publications

New combined DT-CWT and HOG descriptor for static and dynamic hand gesture recognition

New combined DT-CWT and HOG descriptor for static and dynamic hand gesture recognition

Information System for Learning Control in Teaching Russian Sign Language: Process and Data Modeling

Electric Wheelchair Control Using Wrist Rotation Based on Analysis of Muscle Fatigue

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