Real-time sign language framework based on wearable device: analysis of MSL, DataGlove, and gesture recognition

Ahmed, Mohamed A.; Zaidan, B. B.; Zaidan, A. A.; Albahri, O. S.; Al-qaysi, Z.T.; Albahri, A. S.; Salih, Mahmood Maher

doi:10.1007/s00500-021-05855-6

Cited by 37 publications

(7 citation statements)

References 57 publications

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“…Gesture language recognition Translate gesture language to voice or text. [153,[187][188][189][190][191][192] Human-computer interaction Virtual Reality Game Control. [193][194][195] Surgical training.…”

Section: Application Field Referencesmentioning

confidence: 99%

Manipulator Control System Based on Flexible Sensor Technology

Chen,

Wang,

Chen

et al. 2023

Micromachines

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The research on the remote control of manipulators based on flexible sensor technology is gradually extensive. In order to achieve stable, accurate, and efficient control of the manipulator, it is necessary to reasonably design the structure of the sensor with excellent tensile strength and flexibility. The acquisition of manual information by high-performance sensors is the basis of manipulator control. This paper starts with the manufacturing of materials of the flexible sensor for the manipulator, introduces the substrate, sensor, and flexible electrode materials, respectively, and summarizes the performance of different flexible sensors. From the perspective of manufacturing, it introduces their basic principles and compares their advantages and disadvantages. Then, according to the different ways of wearing, the two control methods of data glove control and surface EMG control are respectively introduced, the principle, control process, and detection accuracy are summarized, and the problems of material microstructure, reducing the cost, optimizing the circuit design and so on are emphasized in this field. Finally, the commercial application in this field is explained and the future research direction is proposed from two aspects: how to ensure real-time control and better receive the feedback signal from the manipulator.

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Section: Application Field Referencesmentioning

confidence: 99%

Manipulator Control System Based on Flexible Sensor Technology

Chen,

Wang,

Chen

et al. 2023

Micromachines

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“…Other systems such as the one presented in [50], [51], [52], [53] use gloves as sensors and machine learning approaches for detection and recognition of the sign letters. Some approaches even use Radio Frequency (RF) sensors to detect and recognise sign languages [54].…”

Section: B: Implemented Systemsmentioning

confidence: 99%

Technological Solutions for Sign Language Recognition: A Scoping Review of Research Trends, Challenges, and Opportunities

et al. 2022

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Habitat for the Elderly, and CA19121 Network on Privacy-Aware Audio-and Video-Based Applications for Active and Assisted Living, supported by COST (European Cooperation in Science and Technology). COST (European Cooperation in Science and Technology) is a funding agency for research and innovation networks. Our Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation. www.cost.eu.

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“…Being the recognition of sign language the most complex application identified since it is a visual language that draws on all the main types of recognition through fingerspellings, facial and body gestures in different languages, such as Arabic [43], American [44], Chinese [45], Korean [46] and Indian [39]. Due to the difficulty of sign language recognition, device-based studies have been conducted to improve accuracy [47]- [49]. However, to achieve the speech recognition [50], [51] is needed for both language and speaker identification.…”

Section: ) Applicationsmentioning

confidence: 99%

A Survey on Intelligent Gesture Recognition Techniques

et al. 2022

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Gesture recognition is an ideal means of interaction because it allows users not to have to make contact with any surface, which is a safe and hygienic means, especially in the pandemic situation that is occurring worldwide. However, gesture recognition is not a new discipline and it has been researched for many years but this type of interaction has not succeeded in replacing the keyboard and mouse. It is very useful to know about the advances that are being made with artificial intelligence in gesture recognition to be able to perform a more robust and reliable gesture recognition with a low response time. As it is, deep learning is being integrated into various areas to increase improvement in performance and one such area is artificial intelligence. In this way, there is the possibility that in the future the recognition of gestures will be a viable option as a means of daily interaction for the user and the main objective of this paper is to contribute to that process. For this reason, this study has analyzed 571 papers related to gesture recognition and artificial intelligence. This analysis has extracted relevant information related to scientific production, such as the most productive authors and journals or the most pertinent articles on the subject. Furthermore, we have developed our own model, which shows the relationship between the types of gesture recognition and the artificial intelligence techniques that have been applied for this task.

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Real-time sign language framework based on wearable device: analysis of MSL, DataGlove, and gesture recognition

Cited by 37 publications

References 57 publications

Manipulator Control System Based on Flexible Sensor Technology

Manipulator Control System Based on Flexible Sensor Technology

Technological Solutions for Sign Language Recognition: A Scoping Review of Research Trends, Challenges, and Opportunities

A Survey on Intelligent Gesture Recognition Techniques

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