Glove Based Sign Interpreter for Medical Emergencies

Mugala, Sheila; Jjagwe, Paulus; Asiimwe, Joshua

doi:10.23919/istafrica.2019.8764816

Cited by 4 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, the capability of translating to and from sign language by mobile systems using machine learning and electromyography to recognize the signs [29] or instrumented gloves (smart gloves) which will be thoroughly explained in the upcoming section and leverage depth cameras, including the development of animated avatars to help to show the signs. This technology will make it more accessible to communicate between deaf and hearing people, especially during emergencies, [30] when available certified interpreters can be hard to find. At the same time, paramedics and doctors don't have the ability to sign and communicate.…”

Section: Figure 3 the Deaf Model Of The Communication Processmentioning

confidence: 99%

The Sign Language Interpreting Gloves

Burhani¹,

Prasetyo²

2023

J. of App. Sci. & Adv. Eng.

View full text Add to dashboard Cite

This report’s primary purpose is to provide a detailed overview of the sign language interpreting gloves project. It starts with an introduction, background and history of the deaf community to better understand the needs and issues of the D/deaf community. In the following literature review, the reader will have the chance to learn about the modern-days challenges faced by hearing-impaired people, broadly explained and elaborated. The psychosocial impact of assistive technology and its effectiveness in improving life quality is discussed next. A lack of new technologies assisting the D/deaf community is not hardly noticed. As elaborated in the previously mentioned chapters, sign language interpreting services are expensive and sign language interpreters cannot be easily booked. These and other findings gathered from the research and also by a survey conducted on a group of deaf individuals indicate that sign language interpreting gloves are the new free interpreters-to-be, and the answer to acquiring the so-wanted independence. Evaluated to have such a significant impact, building this assistive technology that can be easily accessed is the project's focus. The working principles, block diagram and flowchart are also described in the methodology section, where building, not one but a pair of gloves is also documented step by step. Finally, results, discussion and future work will tell us more about the importance of interpreting gloves to close the gap between both hearing and deaf communities.

show abstract

Section: Figure 3 the Deaf Model Of The Communication Processmentioning

confidence: 99%

The Sign Language Interpreting Gloves

Burhani¹,

Prasetyo²

2023

J. of App. Sci. & Adv. Eng.

View full text Add to dashboard Cite

show abstract

“…This method achieves the alternation of hand movement, but it is not possible to obtain the changes of hand shapes. In [21][22][23], the authors combined both curvature and velocity sensors, but the classification of hand gesture categories was performed by the conventional comparison structures with simple instruction sets of microcontrollers (e.g. ARV, 8051 with compare and check condition instructions), without evaluation or survey changing of parameters of sensors or machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Fusion Machine Learning Strategies for Multi-modal Sensor-based Hand Gesture Recognition

Doan

Nguyen

2022

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

Hand gesture recognition has attracted the attention of many scientists, because of its high applicability in fields such as sign language expression and human machine interaction. Many approaches have been deployed to detect and recognize hand gestures, like wearable devices, image information, and/or a combination of sensors and computer vision. However, the method of using wearable sensors brings much higher accuracy and is less affected by occlusion, lighting conditions, and complex background. Existing solutions separately utilize sensor information and/or only use sensor information processing and decision-making algorithms over conventional threshold comparison algorithms and do not analyze data or utilize machine learning algorithms. In this paper, a multi-modal solution is proposed that combines information for measuring the curvature of the fingers and sensors for measuring angular velocity and acceleration. The provided information from the sensors is normalized and analyzed and various fusion strategies are used. Then, the most suitable algorithm for these sensor-based multiple modalities is proposed. The proposed system also analyzes the differences between gestures and actions that are almost similar but in fact, they are just normal moving gestures.

show abstract

A Systematic Review on Systems-Based Sensory Gloves for Sign Language Pattern Recognition: An Update From 2017 to 2022

et al. 2022

View full text Add to dashboard Cite

Sign language is the predominant mode of communication for the Deaf community. For the millions of people who suffer from hearing loss around the world, interaction with people who have the ability to hear and do not suffer from hearing impairment or loss is considered as complicated. In line with this issue, technology is perceived as a crucial factor in being an enabler of providing solutions to enhance the quality of life of the Deaf and people with hearing impairment by increasing accessibility. This research aims to review and analyze articles related to sign language recognition based on the sensor-based glove system, in order to identify academic motivations, challenges, and recommendations related to this field. The search for the relevant review materials and articles was performed on four major databases ranging from 2017 to 2022: Science Direct, Web of Science, IEEE Xplore, and Scopus. The articles were chosen based on our inclusion and exclusion criteria. The literature findings of this paper indicate the dataset size to be open issues and challenges for hand gesture recognition. Furthermore, the majority of research on sign language recognition based on data glove was performed on static, single hand, and isolated gestures. Moreover, recognition accuracy typically achieved results higher than 90%. However, most experiments were carried out with a limited number of gestures. Overall, it is hoped that this study will serve as a roadmap for future research and raise awareness among researchers in the field of sign language recognition.

show abstract

Glove Based Sign Interpreter for Medical Emergencies

Cited by 4 publications

References 5 publications

The Sign Language Interpreting Gloves

The Sign Language Interpreting Gloves

Fusion Machine Learning Strategies for Multi-modal Sensor-based Hand Gesture Recognition

A Systematic Review on Systems-Based Sensory Gloves for Sign Language Pattern Recognition: An Update From 2017 to 2022

Contact Info

Product

Resources

About