Interpretation Of Signs Research Articles

SIGNSense: Auditory -Optic Impairment Communication Bridge

Language experts have recognized sign languages as natural languages with the ability to convey human emotions and ideas. Translation from written language into sign videos or extraction of spoken language sentences from sign videos is the aim of sign language translation. Sign language is the principal means of communication for the deaf and hard of hearing community, which comprises 32 million children and 328 million adults worldwide who suffer from hearing impairment. However, the inability of current systems to accurately translate and transmit sign language motions in real-time prevents effective and spontaneous communication. This research provides a revolutionary technique that enables real-time recognition of ISL gestures by integrating natural language processing with cross-modal integration. The methodology uses cutting-edge methods like the Single Shot Multibox Detector (SSD) with MobileNetV2 architecture for data collection, preprocessing, model selection, and training. In real-time inference, the trained model attains an impressive 94% accuracy rate, showcasing strong performance and encouraging outcomes for enhancing communication accessibility for people with hearing impairments

3D printed PEDOT:PSS-based conducting and patternable eutectogel electrodes for machine learning on textiles

The proliferation of medical wearables necessitates the development of novel electrodes for cutaneous electrophysiology. In this work, poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) is combined with a deep eutectic solvent (DES) and polyethylene glycol diacrylate (PEGDA) to develop printable and biocompatible electrodes for long-term cutaneous electrophysiology recordings. The impact of printing parameters on the conducting properties, morphological characteristics, mechanical stability and biocompatibility of the material were investigated. The optimised eutectogel formulations were fabricated in four different patterns —flat, pyramidal, striped and wavy— to explore the influence of electrode geometry on skin conformability and mechanical contact. These electrodes were employed for impedance and forearm EMG measurements. Furthermore, arrays of twenty electrodes were embedded into a textile and used to generate body surface potential maps (BSPMs) of the forearm, where different finger movements were recorded and analysed. Finally, BSPMs for three different letters (B, I, O) in sign-language were recorded and used to train a logistic regressor classifier able to reliably identify each letter. This novel cutaneous electrode fabrication approach offers new opportunities for long-term electrophysiological recordings, online sign-language translation and brain-machine interfaces.

IMPLEMENTASI SENSOR FLEX PADA PENYANDANG DIFABEL MENGGUNAKAN DFPLAYER SEBAGAI INFORMASI AUDIO

Sign language is a form of language that can be learned which prioritizes communication with body language, facial expressions, and some non-sounds. This sign language is widely used by the deaf and mute. However, there is still a lack of institutions teaching sign language, books on sign language, and a tool for learning sign language. This study aims to design a hardware system that can translate sign language and output in the form of audio and display on the LCD. The stages of the research included designing the physical form of the device, testing the sensor flex, testing the MPU6050, testing the Nrf24l01, testing the Dfplayer, testing the Liquid Crystal Display (LCD), and testing the overall device on the A-Z alphabet. The results showed that when the Flex sensor was not indented the average error was 1.23%, and when it was indented the average error was 2.01%. The delay time required in sending data using Nrf24l01 media for conditions without obstructions is 0 seconds with a distance of 1 meter, with a barrier of 9.84 seconds with a distance of 3 meters, at a distance of 3 meters the data cannot be sent.

ИНТЕРПРЕТАЦИЯ КАК МЕТОД ИССЛЕДОВАНИЯ В АРХИТЕКТУРНОМ ПРОЕКТИРОВАНИИ

The architectural community, observing the negative consequences of its technocratic aspirations, reflexively comes to confusion. Architectural work is infected with search, languishes with a thirst for understanding the pressing problems of architecture, its humanistic purpose, the ability to embody and express thoughts and feelings in its inherent language, which only man, the creator and the organic component of the polysyllabic world of architecture is capable of. As a consequence, there is an interested penetration into his ideological, figurative, linguistic mysteries, involving a wide range of anthropological views. Interpretation of signs should be understood as an active process gradually approaching resolution. Various factors can influence this process: for example, contextual conditions, basic knowledge, empirical knowledge or learned practices should be mentioned. Nelson Goodman's approach has proved particularly fruitful for comparative research. Unlike the well-known previous models, Goodman can relate various sign systems to each other, since he works with a single basis, and not with special typologies. The dilemma of interpreting an architectural work without use, apparently, can only be solved by using a higher context of hidden applications that qualify an unused building as a work of architecture. Consequently, hidden contexts of use are found in areas defined by human existence. Interpretations represent only a small part of a large spectrum of the use of signs. The use of signs ranges from invention, discovery and creation through purposeful interpretation processes to everyday use. Understanding manifests itself as a continuous process, in a constant desire to master and penetrate the world, find your way and act. The results of these processes, which are a fleeting excerpt from an ongoing process, can be described as knowledge. This knowledge is now available for further attempts at understanding — provided that it is cultivated and preserved. It also explains how knowledge is lost. If there is no cognitive ability to understand, any written sign systems remain an accumulation of data or information.

Sign Language Interpreter for Physically Challenged People

This study introduces a novel sign language interpreter designed to improve accessibility and communication for those with physical disabilities, particularly those who experience hearing loss. Through the use of cutting-edge artificial intelligence and machine learning algorithms, the technology interprets sign language movements in real-time, removing social, professional, and educational communication barriers. By means of ongoing cooperation among researchers, technology developers, and individuals with physical disabilities, this invention hopes to make a substantial contribution towards the development of a more inclusive society. People of all physical capacities can actively interact, participate, and succeed in such a community. The fundamental ideas of a revolutionary technology that promotes acceptance, equality, and understanding at the formal, academic level are captured in this abstract

Insight into the Relationship Between Motor and Cognitive Symptoms in Essential Tremor

Essential tremor (ET) is a heterogeneous disorder characterized by bilateral upper limbs action tremor and, possibly, neurological signs of uncertain significance, including voluntary movement abnormalities and cognitive disturbances, i.e., the so-called ‘soft’ signs configuring the ET-plus definition. While motor and cognitive disturbances often coexist in ET, their interrelationship remains largely unexplored. Here we aim to further investigate the relationship between motor symptoms, objectively assessed through kinematic analysis, and cognitive dysfunctions in ET. Seventy ET patients underwent clinical examination, as well as kinematic recordings of tremor and finger tapping and a thorough cognitive assessment. We then tested clinic-demographic and kinematic differences between patients with and without cognitive abnormalities, i.e., with mild cognitive impairment (MCI). Correlation analysis served to explore potential associations between kinematic and cognitive data. Forty-three ET patients (61.42%) had MCI. ET-MCI patients exhibited reduced movement velocity during finger tapping compared to those with normal cognition (p < 0.001). Lower movement velocity during finger tapping was associated with poorer cognitive performance. Namely, we observed a correlation between movement velocity and performance on the Babcock Story Immediate and Delayed Recall Test (r = 0.52 and r = 0.45, both p < 0.001), as well as the interference memory task at 10 and 30 s (r = 0.3, p = 0.008 and r = 0.2, p = 0.03). In this study, we have provided data for a better pathophysiological interpretation of motor and cognitive signs in ET, including the role played by the cerebellum or extra-cerebellar areas, which possibly underpin both signs.

The Hermeneutics of Qur’anic Translation into Sign Language: A Study of Inclusive Pedagogy by the Qur'an Indonesia Project

This study investigates the implementation of inclusive Qur’anic pedagogy by the Qur'an Indonesia Project (QIP), with a focus on its Sign Language translation initiative and inclusive teaching methods. QIP, in collaboration with The Little Hijabi Home Schooling, has launched QuranIDprojectISYARAT, a digital-based endeavor aiming to improve access to the Qur’an and its translations, especially for individuals with disabilities. The QuranIDProjectISYARAT program, developed by QIP, targets the deaf community and provides Qur’anic education through platforms like Instagram and YouTube, utilizing audio, visuals, and Sign Language demonstrations. While QIP utilizes Arabic Sign Language for Qur’an translation, their interpretive process prioritizes understanding Qur’anic meanings over morphemes. This study delves into QIP's Qur’an translation process and inclusive teaching practices, particularly focusing on the experiences of deaf or hearing-impaired individuals. By employing a netnography approach and conducting interviews with QIP's YouTube viewers, the research investigates interactions within QIP's online platforms and gathers insights into viewers' perspectives. The findings suggest that QIP's Qur’an translation incorporates Arabic Sign Language and volunteers' interpretations or improvisations of verses, fostering comprehension and a sense of inclusion among learners in their online Qur’anic education initiatives.

Flexible piezoelectric pressure sensor based on BaTiO3-CNT/RTV composite film with filter paper microstructures for monitoring hand motions

Flexible piezoelectric sensors are widely used in various applications such as physiological signal monitoring and human-computer interaction. The present study introduces a BaTiO3-CNT/RTV piezoelectric sensor fabricated using a filter paper template. It incorporates micro-scale fiber stacking and a 1% CNT doping in the microstructure, resulting in a notable enhancement of sensor sensitivity, increasing it from 0.07 V N−1 to 0.69 V N−1, representing an almost tenfold improvement. Furthermore, the study investigates the influence of affecting factors like the flexible substrate of the sensing film, thickness, and mass fractions of various materials on the output voltage. The sensor exhibits superior characteristics such as good repeatability under 5000 cyclic loads, high elongation at break, fast response (80 ms) and recovery times (90 ms), and good linearity. It also demonstrates outstanding sensitivity (12 mV/10°) when monitoring different finger bending states, enabling real-time, sensitive, and reliable hand motion tracking. This sensor holds promising prospects for future developments in the fields of intelligent grasping and sign language translation.

AI Powered Sign Language Translator

The purpose of this effort is to promote equitable environments and allow people with hearing disabilities to communicate in their native language by proposing an AI-based sign language translator. We used a transformer neural network, which can analyze over 500 data points from a person's face and gestures, to translate sign language into text. The translator can expand, produce new datasets, and build models for sign language recognition thanks to our machine learning process. As a proof of concept, we developed an interpreter for emergency calls using more than 200 sign language words. The main goal is to empower people who are deaf to participate in social, political, economic, and cultural spheres of life. We see a lot of people with illnesses, including blindness, deafness, and dumbness, every day. They have trouble interacting with other people. The suggested method can translate sign language into text and voice since this study describes two-way communication between deaf, dumb, and normal individuals. Key Words: Sign Language, Inclusion, Social Development, Artificial Intelligence, Machine Learning.

Developing Podcast-Based Learning Media for English Education Among Deaf Students

This study addresses the educational challenges faced by deaf students in Indonesia, focusing on developing and implementing a specialised Android-based application, "PODclusive," to facilitate English language learning. The research aimed to create an interactive learning tool that incorporates sign language and textual translations in both English and Indonesian, tailored to the needs of deaf learners. The methodology was a mixed-method approach, combining qualitative and quantitative analyses. The research and development (R&amp;D) framework guided the application's design, development, and evaluation. Participants included a purposive sample of 30 deaf students from SLB N Pringsewu, ensuring a representation across different learning outcomes. The findings revealed that the PODclusive application significantly enhanced the learning experience for deaf students. Students demonstrated improved comprehension of English language concepts, facilitated by the application’s use of sign language video content and offline accessibility. The application was evaluated through expert reviews and user testing, receiving high validity scores for content, design, and educational impact. The implications of this study underscore the effectiveness of tailored educational technologies in addressing specific learning needs. The PODclusive application exemplifies how digital tools can enhance accessibility and engagement for deaf students, suggesting that further exploring similar technologies could be beneficial. This research contributes to the broader discourse on inclusive education, advocating for more innovative approaches to support diverse learning environments.

Reescribir para descodificar: traducción radicante en &lt;i&gt;El libro de Tamar&lt;/i&gt; de Tamara Kamenszain y &lt;i&gt;Tratado de arqueología peruana&lt;/i&gt; de Roberto Zariquiey

The purpose of this article is to unveil the gesture of rewriting as an exercise of sign translation, which is presented as one of the territories of radicant writing in the works El libro de Tamar by Tamara Kamenszain and Tratado de arqueología peruana by Roberto Zariquiey. For this purpose, it has been decided to guide the research through a comparative literary study of the corpus, which is achieved based on the definition offered by Nicolas Bourriaud on contemporary art in his book The Radicant (2009). In these works we identify an incursion into that “translation-oriented modernity” that Bourriaud himself proposes, since the acts of appropriation that are developed constitute a translation of past voices, which help to clear the signs of previous passages that are contained in hermetic archives, in order to build the present from them.

How Health Care Organizations Are Implementing Disability Accommodations for Effective Communication: A Qualitative Study

Prior studies have documented that, despite federal mandates, clinicians infrequently provide accommodations that enable equitable health care engagement for patients with communication disabilities. To date, there has been a paucity of empirical research describing the organizational approach to implementing these accommodations. The authors asked US health care organizations how they were delivering these accommodations in the context of clinical care, what communication accommodations they provided, and what disability populations they addressed. In this study, 19 qualitative interviews were conducted with disability coordinators representing 15 US health care organizations actively implementing communication accommodations. A conventional qualitative content analysis approach was used to code the data and derive themes. The authors identified three major themes related to how US health care organizations are implementing the provision of this service: (1) Operationalizing the delivery of communication accommodations in health care required executive leadership support and preparatory work at clinic and organization levels; (2) The primary focus of communication accommodations was sign language interpreter services for Deaf patients and, secondarily, other hearing- and visual-related accommodations; and (3) Providing communication accommodations for patients with speech and language and cognitive disabilities was less frequent, but when done involved more than providing a single aid or service. These findings suggest that, in addition to individual clinician efforts, there are organization-level factors that affect consistent provision of communication accommodations across the full range of communication disabilities. Future research should investigate these factors and test targeted implementation strategies to promote equitable access to health care for all patients with communication disabilities.

Sign Speak: Recogninzing Sign Language with Machine Learning

Sign language serves as a critical means of communication for individuals with hearing impairments, enabling them to integrate into society effectively and express themselves. However, interpreting and recognizing sign language gestures present unique challenges due to the dynamic nature of gestures and spatial dependencies inherent in sign language communication. As a response, the SignSpeak project employs advanced machine learning techniques to address these challenges and enhance accessibility for the deaf and hard of hearing community. The project leverages a diverse dataset sourced from Kaggle, comprising images of sign language gestures captured in various contexts. The integration of advanced algorithms, such as 3D Convolutional Neural Networks (CNNs), and Gated Recurrent Units (GRUs), enables SignSpeak to recognize and interpret sign language gestures accurately and in real-time. This integration allows the model to capture both spatial and temporal features inherent in sign language, thus enabling more robust and accurate recognition. The project encompasses several critical stages, including data preprocessing, model development, training, and evaluation. Data preprocessing involves converting the image data into a suitable format and applying augmentation techniques to enhance the diversity and robustness of the dataset. Model development entails designing a deep learning architecture that combines CNNs and GRUs to effectively capture spatial and temporal dependencies in sign language gestures. Training the model involves optimizing parameters and hyperparameters to achieve optimal performance. Evaluation metrics such as accuracy, F1 score, and recall are utilized to assess the model's performance on both training and validation datasets. The trained model is then tested on a separate test dataset to evaluate its real- world performance and generalization ability. Experimental results demonstrate the efficacy of the SignSpeak approach in accurately recognizing and interpreting sign language gestures. The model achieves high accuracy scores, demonstrating its potential to enhance accessibility and inclusion for individuals with hearing impairments. By providing real-time translation of sign language into text or speech, SignSpeak contributes to breaking down communication barriers and promoting equal participation for all members of society.

Advancements in Vision-Based Sign Language Recognition: A Comprehensive Review

As nearly 70 million individuals suffer from disabling hearing loss, sign language serves as an important means of communication. Unfortunately, the lack of proficiency in sign language among the general population hinders meaningful interactions with those who rely on it. This paper presents an extensive analysis of the cutting-edge methodologies in sign language translation, with the ultimate goal of facilitating effective communication between sign language users and the broader community. In addition to reviewing state-of-the-art approaches, this work also investigates into the challenges and limitations faced by gesture recognition research. Overall, it is expected that the study may provide readers and researchers with a guide for future research and creation in the field of sign language recognition.

Translating Gestures: Utilizing CNN to Enhance ASL Communication and Understanding

This research introduces a system for translating American Sign Language (ASL) utilising Convolutional Neural Networks. (CNNs). The system under consideration has the capability to identify manual gestures executed by persons communicating in American Sign Language (ASL) and subsequently convert them into written language, thereby facilitating uninterrupted communication between the deaf and hearing populations. The CNN model was validated and trained using a set of data made up of 78,300 pictures of movements in the ASL Alphabet written in American Sign Language (ASL). In order to improve the performance of the model, the pre-processing of the data included a number of stages, such as converting the photos to grayscale, normalising the pixel values, and enhancing performance of the model. In order to ease classification, fully connected layers were added after a succession of pooling and convolutional layers in CNN's design. After 15 epochs of training, the model attained a validation accuracy of 99.85%. The findings of this research demonstrate the viability of employing Convolutional Neural Networks (CNNs) in the creation of precise and effective American Sign Language (ASL) translation systems, which can serve as a means of communication for people with auditory impairments.

GestureFusion: A Gesture-based Interaction System

Abstract: In an era of increasingly sophisticated human-computer interaction, the ability to comprehend and interpret hand gestures has become a pivotal element for bridging the gap between humans and machines. This project, titled ”GestureFusion: A Gesture-based Interaction System,” seeks to contribute to this rapidly evolving field by developing a comprehensive system for recognizing and categorizing hand gestures through the application of computer vision techniques. The primary objective of this project is to create a versatile and extensible hand gesture recognition system that can be employed in various domains such as virtual reality, robotics, sign language interpretation, and human-computer interface design. To achieve this, the project employs state-of-the-art computer vision algorithms and deep learning techniques. Key components of this project include data collection and annotation, model training and optimization, the development of a user-friendly application programming interface (API) for integration into diverse applications.

American Sign Language Translation from Deaf-Mute People Based on Novel System

This paper presents a system to translate gestures from the American Sign Language alphabet using an instrumented wearable glove. This system represents an attempt to utilize a slide potentiometer in sign language translating. The hardware part of the system consists of five slide potentiometers and two force-sensitive resistors, which are best positioned on a glove, based on the analysis of American Sign Language (ASL) letters. In the software part a neural network is used, which was built and trained using Google Colab and Python as the programming language. The performance of the system was tested on three data sets with different numbers of samples. After that, the letters corresponding to the gestures performed are displayed on a computer screen in real time.

CHALLENGES IN ONLINE LEARNING ENVIRONMENTS: FROM THE PERSPECTIVE OF HEARING-IMPAIRED UNIVERSITY STUDENTS AND THEIR LECTURERS

The global COVID-19 pandemic has led to significant challenges in Turkey, as in many other countries, in the transition from face-to-face to online education at all levels of education. This shift has brought forth numerous obstacles, particularly for d/Deaf and hard-of-hearing (d/Dhh) university students. Hence, this research aims to identify the challenges encountered during online learning amidst the pandemic at the School for the Handicapped (SfH), the only institution in Turkey providing vocational higher education to hearing-impaired students. Conducted as a qualitative case study at the SfH, the research involved 20 hearing-impaired students and 19 faculty members as participants. Data obtained from the Mergen Anadolu Learning Management System platform, WhatsApp Platform and other platforms, was used for online education. Interviews, open-ended questionnaires, and the researcher’s journal were compiled and analyzed inductively. Findings from the study highlighted a multitude of challenges encountered in the online learning process, affecting both faculty members and students at the SfH. The challenges encountered by participants in online learning at the SfH were found to be related to difficulty in understanding lessons, lack of subtitles and sign language translation, difficulty accessing computers and software, Internet problems, low camera usage, low motivation, and lack of interaction. Keywords: covid -19 pandemic, case study, hearing impaired, online learning

Academic Barriers of Students with Hearing Impairment at the Undergraduate Level

This study addresses the academic barriers of students with hearing impairment at a private institution. A competent generation depends on education; however, SWHI has particular hurdles in getting higher education. Communication, socialization, curriculum, instructions, learning, and support services are examined as significant factors in academic performance. The census-sampled research examines demographics including age, gender, and disability level in 41 SWHI. The researcher uses sign language interpreters to collect data. Quantitative investigations refine SWHI views on academic impediments. Detail frequency distributions and statistical tests like t-tests reveal demographic trends and variances. Communication, socialization, curriculum adaptability, and educational structural impediments were found. Library, internet, and assistive technology reduce academic challenges, according to the study. The research intends to influence policy-making to make SWHI higher education more accessible by illuminating these complexities.

Sign Language Recognition using CNN

Abstract: The sign language Recognition System is a technology that bridges the gap between the deaf people and the hearing world. Sign language is a vital mode of expression for deaf people, yet effective communication remains a challenge. This project aims to develop a robust Sign Language Recognition System capable of accurately translating sign language gestures into text. The system utilizes a deep learning approach, specifically convolutional neural networks (CNNs) to analyze video input of sign language and then generate the corresponding output. The project involves data collection, pre-processing, segmentation, feature extraction, model training, and evaluation. The proposed SLR system has the potential to enhance. Communication and accessibility for deaf individuals, promoting inclusivity and improving their quality of life. The project represents a comprehensive exploration of both technical and ethical aspects in the realm of computer vision and deep learning applications.