Deaf Research Articles

Neurotopographical Transformations: Dissecting Cortical Reconfigurations in Auditory Deprivation.

Within the intricate matrices of cognitive neuroscience, auditory deprivation acts as a catalyst, propelling a cascade of neuroanatomical adjustments that have, until now, been suboptimally articulated in extant literature. Addressing this gap, our study harnesses high-resolution 3 T MRI modalities to unveil the multifaceted cortical transformations that emerge in tandem with congenital auditory deficits. We conducted a rigorous cortical surface analysis on a cohort of 90 congenitally deaf individuals, systematically compared with 90 normoacoustic controls. Our sample encompassed both male and female participants, ensuring a gender-inclusive perspective in our analysis. Expected alterations within prototypical auditory domains were evident, but our findings transcended these regions, spotlighting modifications dispersed across a gamut of cortical and subcortical structures, thereby epitomizing the cerebral adaptive dynamics to sensory voids. Crucially, the study's innovative methodology integrated two pivotal variables: the duration of auditory deprivation and the extent of sign language immersion. By intersecting these metrics with structural changes, our analysis unveiled nuanced layers of cortical reconfigurations, elucidating a more granulated understanding of neural plasticity. This intersectional approach bestows a unique advantage, allowing for a discerning exploration into how varying durations of sensory experience and alternative communication modalities modulate the brain's morphological terrain. In encapsulating the synergy of neuroimaging finesse and incisive scientific rigor, this research not only broadens the current understanding of adaptive neural mechanisms but also paves the way for tailored therapeutic strategies, finely attuned to individual auditory histories and communicative repertoires.

Decoding auditory deprivation: resting-state fMRI insights into deafness and brain plasticity.

Deafness, as a profound manifestation of sensory deprivation, prompts a cascade of intricate cerebral adaptations. In this study, involving 35 deaf individuals and 35 hearing controls, we utilized resting-state functional magnetic resonance imaging (rs-fMRI) to delve into the depths of functional connectivity nuances distinguishing deaf individuals from their hearing counterparts. Leading our analytical approach was the application of multi-voxel pattern analysis (fc-MVPA). This advanced method provided a refined perspective, revealing amplified neural connectivity within the deaf population. Notably, regions such as the left postcentral somatosensory association cortex, the anterior and posterior corridors of the left superior temporal gyrus (STG), and the left mid-temporal lobe were identified as hotspots of heightened connectivity. Further, fc-MVPA shed light on intricate interaction effects, which became more pronounced when examining variables such as the duration of auditory deprivation and the extent of sign language exposure. These interactions were particularly evident in the premotor and left frontal mid-orbital regions. Complementing this, seed-based connectivity assessments illuminated pronounced coupling dynamics within the left STG spectrum. Concurrently, local correlation (LCOR) value analysis in the deaf group revealed significant shifts in the right superior STG and bilateral precuneus. In addition, amplitude of low-frequency fluctuation (ALFF) evaluations indicated modulations in the bilateral mid cingulum and left superior mid frontal gyrus. This comprehensive, fc-MVPA-driven exploration uncovers the multifaceted functional adaptations resulting from deafness, highlighting the profound plasticity of the human brain and its potential implications for targeted rehabilitative strategies.

Do Deaf Women Feel Safe on Campus? A Qualitative Case Study of One UK University

With increasing attention to sexual misconduct on university campuses, universities have focused attention on campus safety, but safety systems are based on assumptions that people who live and work on campuses are able-minded and able-bodied. Any contemplation of campus safety needs to consider the intersectional experiences of students and staff. Considering the experiences of people from minoritised groups can provide insights into socially constructed notions of safety on campus. One such minoritised group is deaf women. Using the broader concept of safety to mean ‘the state of being ‘safe’ and the condition of being protected from any harm or other danger’, this paper reports on interviews conducted with five deaf women who work or study at one UK university to examine whether they feel safe on the campus. Four key themes were identified: (i) safety as a woman, (ii) safety as a deaf person; (iii) intersectional experiences of safety as a deaf woman; and (iv) accessible safeguarding. These five deaf women reported having more concerns because they are deaf rather than because they are women, but noted that the intersectional experience of being deaf and a woman create deeper safety concerns. Recommendations are made for university campuses to ensure that their safeguarding practices are accessible.

An Adam based CNN and LSTM approach for sign language recognition in real time for deaf people

Hand gestures and sign language are crucial modes of communication for deaf individuals. Since most people can't understand sign language, it's hard for a mute and an average person to talk to each other. Because of technological progress, computer vision and deep learning can now be used to count. This paper shows two ways to use deep knowledge to recognize sign language. These methods help regular people understand sign language and improve their communication. Based on American sign language (ASL), two separate datasets have been constructed; the first has 26 signs, and the other contains three significant symbols with the crucial sequence of frames or videos for regular communication. This study looks at three different models: the improved ResNet-based convolutional neural network (CNN), the long short-term memory (LSTM), and the gated recurrent unit (GRU). The first dataset is used to fit and assess the CNN model. With the adaptive moment estimation (Adam) optimizer, CNN obtains an accuracy of 89.07%. In contrast, the second dataset is given to LSTM and GRU and a comparison has been conducted. LSTM does better than GRU in all classes. LSTM has a 94.3% accuracy, while GRU only manages 79.3%. Our preliminary models' real-time performance is also highlighted.

Breaking Barriers: Hand Gesture Vocalizer for the Deaf and Mute

This study addresses communication challenges faced by deaf and mute individuals by exploring the feasibility of utilizing flex sensors and the Random Forest algorithm for hand gesture vocalization. The background underscores the significance of accessible communication tools in enhancing the lives of those with hearing and speech impairments. The study's purpose is to assess the effectiveness of flex sensors in detecting hand gestures and the Random Forest algorithm's potential to generate vocalized speech corresponding to these gestures. The methodology involves data collection from flex sensors through Arduino, Random Forest model training, and accuracy evaluation in gesture recognition. Promising results indicate the model's high accuracy in classifying diverse hand gestures. The study emphasizes the technology-driven solution's importance in bridging communication gaps for those with impairments. Combining flex sensors and the Random Forest algorithm offers an intuitive communication tool, transforming interactions for deaf and mute individuals. Consideration for real-world scenarios and user diversity during system development is highlighted, crucial for practical accuracy. Beyond individual communication, the study's implications span education, employment, and social integration for people with disabilities. Implementing this technology in education fosters inclusive environments, empowering deaf and mute students to engage actively. The integration of flex sensors and the Random Forest algorithm holds immense promise, revolutionizing communication, and life quality. As an accessible gesture-based vocalization tool, it can reshape societal perspectives, fostering inclusivity and empathy. The study advocates continuous research and development, urging widespread technology adoption to create an inclusive society valuing diversity.

Revealing sign language for individuals who are deaf and mute through the utilization of neural networks

Living without effective communication poses significant challenges for humans. Individuals employ various methods to convey and share their thoughts and ideas between the sender and receiver. Two of the most prevalent means of communication are verbal speech, which relies on auditory perception, and non-verbal communication through gestures involving bodily movements such as hand gestures and facial expressions. Sign language, specifically categorized as a gestural language, is a unique form of communication that relies on visual perception for understanding and expression. While many individuals incorporate gestures into their communication, for deaf individuals, sign language is often their primary and essential means of communication. Individuals who are deaf and dumb rely on communication to interact with others, gain knowledge, and engage in their surroundings. Sign language serves as a crucial link that reduces the distance between them and the broader society. In order to enhance this communication, we've created models with the ability to identify sign language gestures and translate them into conventional text. Through the training of these models on a dataset employing neural networks, remarkable outcomes have been attained. This technology enables individuals, without prior knowledge of sign language, to understand the intentions and messages of individuals with disabilities, fostering greater inclusivity and accessibility in our society. Three algorithms were used to achieve this work and the findings show very good outcomes, i.e. Random Forest at 98%, Logistic Regression at 99%, and Decision Tree at 91%. Keywords: Machine Learning, Sign Language, Random Forest, Neural Network, Logistic Regression Deaf and Dumb, Decision Tree

Sign Bilingualism Method Bridging the Gap between Language and Literacy among Deaf Learners

The education of Deaf/d students faced unique education challenges, particularly in the language and literacy development. This paper provides a comprehensive overview current education system of Deaf. It addresses critical issues such as language acquisition, literacy proficiency, mathematical learning, and logical problem-solving skills among Deaf learners. The paper underscores the importance of the sign bilingualism method in the education of Deaf individuals, emphasizing its role in bridging the gap between language and literacy development. It stresses the pivotal role of Sign Bilingualism in achieving objective language development and its use in literacy development. By recognizing the significance of Sign Bilingualism in Deaf education, educators and stakeholders can contribute to a more inclusive learning environment for Deaf learners.

SIGN LANGUAGE OF THE DEAF PEOPLE: A STUDY ON PUBLIC UNDERSTANDING

Today, when various regulatory documents support sign language research, there is a lack of both teaching materials and research on deaf sign language and public understanding. The study aims to determine the public’s proficiency or competence in the Latvian sign language. The research revealed people’s basic knowledge of Latvian sign language grammar, their ability to communicate and understand deaf people, their attitudes and opinions about the process, and the necessity of learning sign language. Information was studied in four blocks of questions: society’s attitude towards deaf sign language, society’s knowledge of deaf sign language, communication skills with a deaf person, and opinion on the development of deaf sign language and its necessity. The sample of the study is 500 respondents, who represent the opinion of the population between the ages of 16 and 60, observing the socio-demographic parameters of the target group of the study. An empirical research method was used in the study; to collect data, an electronic survey was conducted. In general, the study confirms that society shows a positive attitude towards Latvian sign language as a means of communication for deaf people. At the same time, the data reveal poor knowledge of sign language and its grammar. It was concluded that not only sociological research is needed on public perceptions of deaf sign language, but also various educational events for the rest of society should be promoted, including seminars, courses, and conferences.

Silent struggles to self-manage high blood pressure among deaf sign language users: a qualitative study.

Tailored self-management support of hypertension, considering language and communication, is important for minorities, specifically in the deaf community. However, little is known about the experiences of hypertension self-management in deaf individuals who use sign language. This study aimed to explore the factors and processes of self-management in deaf sign language users with hypertension. Ten men and women who used sign language participated in this study. Data were collected using in-depth personal interviews conducted in the presence of a sign language interpreter between November 2022 and February 2023. All interviews were recorded and transcribed for conventional content analysis. Qualitative analyses identified four categories related to the self-management of hypertension among participants: personal factors (chronic hand pain, unique language and communication, and efforts to turn crisis into opportunities), family and socioeconomic factors (family support and financial burden of living), challenges (limited health literacy and alienation from health education), and desire for health education considering the deaf community. The results of this study suggest that family support, socioeconomic status, hand pain, and health literacy should be considered for the planning and development of health education on self-management of hypertension in deaf individuals. In addition, this health education requires cooperation with qualified sign language interpreters in healthcare settings.

Development of embodied listening studies with multimodal and wearable haptic interfaces for hearing accessibility in music

IntroductionThe intersection of hearing accessibility and music research offers limited representations of the Deaf and Hard of Hearing (DHH) individuals, specifically as artists. This article presents inclusive design practices for hearing accessibility through wearable and multimodal haptic interfaces with participants with diverse hearing backgrounds.MethodsWe develop a movement-based sound design practice and audio-tactile compositional vocabulary, co-created with a Deaf co-designer, to offer a more inclusive and embodied listening experience. This listening experience is evaluated with a focus group whose participants have background in music, dance, design, or accessibility in arts. By involving multiple stakeholders, we survey the participants' qualitative experiences in relation to Deaf co-designer's experience.ResultsResults show that multimodal haptic feedback enhanced the participants' listening experience while on-skin vibrations provided more nuanced understanding of the music for Deaf participants. Hearing participants reported interest in understanding the Deaf individuals' musical experience, preferences, and compositions.DiscussionWe conclude by presenting design practices when working with movement-based musical interaction and multimodal haptics. We lastly discuss the challenges and limitations of access barrier in hearing accessibility and music.

Multimodal Real-Time patient emotion recognition system using facial expressions and brain EEG signals based on Machine learning and Log-Sync methods

Human Machine Interface (HMI) depends on emotion detection, especially for hospitalized patients. The emergence of the fourth industrial revolution (4IR) has heightened the interest in emotional intelligence in human–computer interaction (HCI). This work employs electroencephalography (EEG), an optical flow algorithm, and machine learning to create a multimodal intelligent real-time emotion recognition system. The objective is to assist hospitalized patients, disabled (deaf, mute, and bedridden) individuals, and autistic youngsters in expressing their authentic feelings. We fed our multimodal feature fusion vector to a classifier with long short-term memory (LSTM). We distinguished six fundamental emotions: anger, disgust, fear, sadness, joy, and surprise. The fusion feature vector was created utilizing the patient's geometric facial characteristics and EEG inputs. Utilizing 14 EEG inputs, we used four-band relative power channels, namely alpha (8–13 Hz), beta (13–30 Hz), gamma (30–49 Hz), and theta (4–8 Hz). We achieved a maximum recognition rate of 90.25 percent using just facial landmarks and 87.25 percent using only EEG data. When both facial and EEG streams were examined, we achieved 99.3 percent accuracy in a multimodal method.

THE USE OF SIGN LANGUAGE AS A MEDIA FOR DELIVERING INFORMATION ON NATIONAL TELEVISION NEWS BROADCASTS

Sign language is communication using non-verbal words which are delivered without words but with body movements/body language or facial expressions. Sign language has evolved into a practical mode of communication that forms the basis of deaf communities. It is a visual-spatial language based on positional and visual elements including the shape of digits and hands, position, orientation, limb, and gestures. This signal can be seen in a small box at the corners of television newscasts. This research reviews the use of sign language as media in delivering information (news) in TV broadcasts. This research was library research. The analysis showed that news programs broadcast by television stations become one of the providers of information. The existence of sign language facilities in news programs at this time at least minimizes gaps in the right to access information among persons with deaf disabilities. Several previous studies show that sign language is media information read by the news anchor to the TV audience (viewers) with the intermediary of a sign language interpreter. Sign language plays an active role for deaf people based on three effects of mass communication which provide direct cognitive, affective, and conative effects for deaf people. There is a relationship between the influence of the use of sign language in news programs on deaf people such as attracting the attention of deaf audiences with an easy visualization. A presentation of the news is enough to meet the information needs of the deaf person by continuing to prioritize the latest information. 

Pengaruh Pola Asuh Orang Tua terhadap Penerimaan Bahasa Reseptif Anak Tunarungu

This case study is being conducted after long observation to a child named AB. He is eight (8) years old and he doesn’t being a part of any schools. He has a problem related to the hearing. Doctor has diagnosed him as a deaf person. In his everyday life, he doesn’t do any activities in normal way such as take everything without permission, he is not able communicate with each other, and he can’t express his willing. After doing depth interview to his mother, unfortunately, his mother doesn’t apply the right parenting for him. That is why it becomes a reason that he does such things. All of these are caused by the failure of his receptive language. Normal children will process the information he got and then express it in oral language. AB doesn’t have this ability because there is a problem with his hearing. In other words, his receptive language is not running well. Referring to this condition, every parent should give best and more attention to every child with special need like AB. Some scientists say that a child like AB has the same chance to get good parenting like others. Many stimulus and exercises must given to him in order he can do many thing by himself and he can be accepted by his environment.

The design of the Deaf in Touch Everywhere (DITE)TM mobile application with Deaf and interpreter communities in Malaysia

Background Ineffective communication with Deaf individuals in healthcare settings has led to poor outcomes including miscommunication, waste, and errors. To help address these challenges, we developed a mobile app, Deaf in Touch Everywhere (DITETM) which aims to connect the Deaf community in Malaysia with a pool of off-site interpreters through secure video conferencing. Objectives The aims of this study were to (a) assess the feasibility and acceptability of measuring unified theory of acceptance and use of technology (UTAUT) constructs for DITETM with the Deaf community and Malaysian sign language (BIM) interpreters and (b) seek input from Deaf people and BIM interpreters on DITETM to improve its design. Methods Two versions of the UTAUT questionnaire were adapted for BIM interpreters and the Deaf community. Participants were recruited from both groups and asked to test the DITE app features over a 2-week period. They then completed the questionnaire and participated in focus group discussions to share their feedback on the app. Results A total of 18 participants completed the questionnaire and participated in the focus group discussions. Ratings of performance expectancy, effort expectancy, facilitating conditions and behavioural intention were high across both groups, and suggestions were provided to improve the app. High levels of engagement suggest that measurement of UTAUT constructs with these groups (through a modified questionnaire) is feasible and acceptable. Conclusions The process of engaging end users in the design process provided valuable insights and will help to ensure that the DITETM app continues to address the needs of both the Deaf community and BIM interpreters in Malaysia.

From echo chamber to orchestra: Orchestrating job empowerment for deaf employees in Jeddah

Deaf individuals in the Saudi labour market face significant hurdles, adversely impacting their professional integration. This study investigates the reality of job empowerment for Deaf employees from their perspective in Jeddah, aiming to illuminate the nuances of their experiences. A qualitative approach employed interviews with twelve Deaf employees, purposively sampled from the community. The findings paint a complex picture. While overall relations appear positive, a stark gender disparity emerges in workplace power dynamics and opportunities. Deaf males enjoy greater autonomy, wield influence and navigate responsibilities relatively easily. Deaf women, however, encounter discriminatory practices, are often excluded from crucial decision-making processes and professional engagements. This study underscores the urgent need for concrete measures to combat workplace gender bias and empower Deaf females. Business institutions must prioritize equal opportunities for employment, training, and development by fostering inclusive work environments and providing targeted support for Deaf females. Such findings provide an in-depth understanding of the challenges and opportunities faced by Deaf employees, particularly navigating the complexities of gender dynamics within the professional sphere. Further research is warranted to explore effective strategies for fostering equitable and empowering workplaces for Deaf individuals of all genders, ultimately promoting their successful integration into the Saudi labour market.

Fatores associados ao escore de ansiedade de mães surdas e ouvintes

ABSTRACT Purpose To associate maternal anxiety with sociodemographic factors, breastfeeding practices, oral habits, and the child’s entry into daycare among deaf and hearing (non-deaf) mothers. Methods This retrospective comparative cross-sectional study included 116 mothers (29 deaf and 87 hearing) of children aged between two and five years. Deaf mothers belonged to a reference center in the city, while hearing mothers were contacted in public daycares where their children were enrolled. Mothers underwent interviews covering socio-economic factors and child development-related aspects. Additionally, they completed the Brazilian Beck Anxiety Inventory, adapted for both deaf and hearing individuals, serving as instruments to assess anxiety. The Kolmogorov-Smirnov normality test, Kruskal Wallis test, Mann-Whitney test, and Poisson Regression were employed for statistical analyses (p<0.05). Results Deaf mothers exhibited anxiety scores one and a half times higher than hearing mothers. Moreover, mothers of children with thumb-sucking habits showed higher anxiety scores, while mothers whose children started attending daycare as infants demonstrated lower anxiety scores compared to mothers of children without such habits and who did not attend daycare. Conclusion Deaf mothers displayed higher anxiety levels when compared to hearing mothers. Children’s behaviors, such as thumb-sucking habits, and early enrollment in daycare during the first year of life influenced maternal anxiety.

Recognition of Wh-Question Sign Gestures in Video Streams using an Attention Driven C3D-BiLSTM Network

Human sign language is a visual and gestural means of communication used by people with hearing impairments to interact with others. It has the potential to enable interaction between individuals who struggle with verbal communication and those who lack the essential proficiency to comprehend sign language. Wh-questions constitute a substantial portion of daily sign language interactions. Automatic recognition of Wh-question words in sign language, encompassing interrogative words, has received limited attention in gesture recognition. In this context, a novel dataset named the American Question Sign Video dataset (AQSVd) has been introduced as a significant contribution. Recognizing Wh-question signs in video streams empowers individuals to effectively convey messages through hand movements and gestures, fostering inclusivity and accessibility in communication for deaf and hearing-impaired individuals. The paper proposes a Deep Convolutional-3D BiLSTM Multi-head Attention network for recognizing American Wh-Question word sign gestures in video streams. Incorporating Multi-head attention in the proposed model enhances its ability to capture intricate spatial and temporal features, essential for accurate gesture recognition. The proposed model is thoroughly evaluated across a range of datasets, such as INCLUDE50, WLASL-100, and our AQSVd dataset, achieving remarkable results. Specifically, our model demonstrated an outstanding 98.91% validation accuracy on the AQSVd dataset. The C3D-BiLSTM MHAttention model outperforms other state-of-the-art models, demonstrating its superiority in sign language recognition tasks. The proposed dataset and C3D-BiLSTM Multi-head Attention model contribute significantly to this field, offering potential benefits in education, human-robot interaction, and overall communication for the hearing-impaired community.

Factors associated with the anxiety score of deaf and hearing mothers.

To associate maternal anxiety with sociodemographic factors, breastfeeding practices, oral habits, and the child's entry into daycare among deaf and hearing (non-deaf) mothers. This retrospective comparative cross-sectional study included 116 mothers (29 deaf and 87 hearing) of children aged between two and five years. Deaf mothers belonged to a reference center in the city, while hearing mothers were contacted in public daycares where their children were enrolled. Mothers underwent interviews covering socio-economic factors and child development-related aspects. Additionally, they completed the Brazilian Beck Anxiety Inventory, adapted for both deaf and hearing individuals, serving as instruments to assess anxiety. The Kolmogorov-Smirnov normality test, Kruskal Wallis test, Mann-Whitney test, and Poisson Regression were employed for statistical analyses (p<0.05). Deaf mothers exhibited anxiety scores one and a half times higher than hearing mothers. Moreover, mothers of children with thumb-sucking habits showed higher anxiety scores, while mothers whose children started attending daycare as infants demonstrated lower anxiety scores compared to mothers of children without such habits and who did not attend daycare. Deaf mothers displayed higher anxiety levels when compared to hearing mothers. Children's behaviors, such as thumb-sucking habits, and early enrollment in daycare during the first year of life influenced maternal anxiety.

How Iranian Deaf Individuals Discuss Domestic Violence and Its Risk Factors

How Iranian Deaf Individuals Discuss Domestic Violence and Its Risk Factors

The Effect of Delayed Language Acquisition on Criminal Behavior of Deaf Individuals Involved in the Criminal Justice System

Research indicates that approximately 90% of pre-lingual deaf children are born to hearing parents. Further, according to the 2021 American Community Survey (ACS), about 3.6% of the U.S. population, or about 11 million individuals, consider themselves deaf or have serious difficulty hearing. For many of those children born to hearing parents, they are not learning or acquiring language from their parents as their parents do not know or use sign language. Often times, these parents have been advised (mistakenly) to not learn and use sign language with their children because of the belief that signing prevents speech and mainstream inclusion. This lack of language skills can have a deleterious effect on deaf children and lead to “an impoverished or distorted relationship between parent and child. This can lead to challenging behavior in adulthood. This paper will explore the nexus between language deprivation/delayed language development and the subsequent risk factors associated with criminal behavior.