Systemic exclusion remains a significant barrier for individuals experiencing vulnerabilities, limiting their participation in service systems—configurations of interdependent actors, institutions, and infrastructures that shape service environments. This study examines how inclusive service systems evolve through dynamic, multi-actor engagement by integrating systems thinking, transformative service research, and macro-social marketing. Using an abductive, multi-method design, we focus on the Deaf community in Indonesia as a salient case and combine interviews with Deaf and hearing individuals, netnographic analysis of Deaf-oriented online content, rapid ethnographies at community events, and computational topic modeling of user-generated discourse. The analysis uncovers four evolving modalities of system transformation—Surface-Level Engagement, In-Depth Exploration, Co-Creative Participation, and Sustained Involvement—which trace how micro-level relational practices and communicative adjustments accumulate into meso- and macro-level shifts in structures and norms. We conceptualize inclusion as an emergent, iterative process rather than a stable end-state and propose a scalable framework to diagnose and facilitate inclusive transformation in complex service environments. The study advances theoretical perspectives on inclusive service systems and macro-social marketing and offers actionable guidance for designing and governing service systems that give marginalized groups an active role in shaping services.

Despite Alzheimer disease and related dementias/disorders (ADRD) being a substantial public health concern, the Deaf community remains underrepresented in ADRD research. We examined the extent and nature of existing literature on ADRD in the Deaf community. We conducted a scoping review following Arksey and O'Malley's framework, adhering to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-analyses. We searched 7 databases for peer-reviewed studies published in English, with no restrictions on publication date or study design. Inclusion studies addressed ADRD within the Deaf community and examined aspects such as diagnosis, care, and communication. Our search identified 435 articles, of which 16 met the inclusion criteria. Studies primarily originated from the United Kingdom and Finland, followed by the United States. Most used qualitative methodologies and explored lived experiences and communication barriers. Common themes included limited access to culturally competent care and caregiving challenges. Few studies highlighted diagnostic interventions but noted a general scarcity of resources tailored to Deaf populations. Notable gaps emerged in geographic focus, the range of studied variables, and the lack of validated sign language-based diagnostic instruments and interventions. We recommend expanding research beyond the Global North (ie, higher-income industrialized countries with greater access to resources), incorporating more longitudinal and intersectional approaches, and tailoring resources for Deaf communities. The review underscores the need for culturally competent care, improved diagnostic tools, and policy reforms to address ADRD-related disparities in the Deaf community. Future research should prioritize inclusive methodologies and community-driven interventions to enhance health outcomes and equity for Deaf individuals affected by ADRD.

This study explored how sign language use and social connectedness influenced the life satisfaction of twelve British Deaf community members (n = 12, m = 2, f = 10, aged 23-79). During in-person and remote interviews, participants compared experiences of sign language use and social connectedness with experiences of spoken language and exclusion. Reflexive thematic analysis generated two main themes. (1) Gaining Deafness: which explored how sign language use and social connectedness created positive perceptions of deafness, enabling authenticity and agency. (2) Empowering Deaf pride: which examined how feeling connected to sign language and Deaf culture produced Deaf pride and self-advocacy, empowering participants. These findings support the implementation of sign language in early education for all children. The findings call for the social reconstruction of deafness, and for deaf-led development of inclusive definitions and terminology. This research provides new qualitative evidence on how sign language and Deaf cultural connectedness enhance the life satisfaction of Deaf adults.

This article examines how deaf students and interpreters experience positioning within mainstream educational settings, with a focus on environments where a critical mass-defined as multiple deaf students and interpreters-is present. Drawing on interviews with 41 formerly mainstreamed deaf individuals and interpreters, the study explores how critical mass influences engagement, participation, and perceived membership in the school community. Findings suggest that critical mass enhances access to Deaf Community Cultural Wealth (DCCW) and fosters collaborative positioning. These environments strengthen support systems for both deaf students and interpreters. These environments also promote professional development among interpreters and contribute to more inclusive school climates. The study is framed by positioning theory and DCCW, offering insights into how systemic and interpersonal dynamics shape educational experiences. Implications are discussed for both deaf education and interpreter education communities.

Every event that presents itself in society and shapes a habit is a way that can be brought forth to draw a meaning. Through the actions carried out by those who have hearing limitations, especially the deaf, their ways and actions make interpersonal communication forms intriguing to be deeply examined. With all their forms and limitations, spaces of communication always manifest and are given their meanings. The way the deaf community communicates, whether through sign language or alternative methods, highlights the richness of their social interactions and the unique construction of meaning that occurs in these interactions. Meaning in this context is not fixed, but rather fluid and dynamic, shaped by both individual and collective experiences. According to Alfred Schutz's phenomenological approach, meaning is a process that emerges from individuals’ lived experiences and their interactions with others. It is something that adapts to the conditions and contexts in which it is embedded. Schutz’s concept of “meaning construction” emphasizes that meaning is not only a subjective experience but also a social one, grounded in shared understanding.

Understanding and automatically recognizing sign language is crucial for equal opportunities for deaf and hard-of-hearing individuals in society. In the world of Arabic sign language communication, people share their ideas, thoughts, and feelings and engage in meaningful daily interactions. While artificial intelligence and computer vision technologies have improved, studies focused on Arabic sign language processing have come first and continue to be a research frontier compared to other world languages. This research proposed a methodology to leverage the HSV color space model for initial segmentation to eliminate background noise to refine the input data. This process segments the hand or semantic sign area. Then, the data is passed onto a Convolutional Neural Network (CNN) for precise sign classification. This method’s novelty is the coordinated interplay of differential data simplification through color space manipulation and the profound representational power of the CNN for improved overall system performance for hood sign recognition. The research also seeks to fill an existing gap in the field of Arabic signal processing by presenting an integrated model, opening up broad horizons for applications in the fields of education, healthcare, and smart government services. The proposed method showed promising results, as the results of the used criteria ranged between Accuracy 95.3%, Precision 93.5%, Recall 92.8%, and F1-score 93.1%. Background: Recognizing sign languages ​​has always been a vital topic in societies, especially with the recent massive expansion of the deaf and mute community. It serves as the main form of communication for over 70 million deaf and hard-of-hearing individuals globally. Thus, the development of simple, accurate, and effective methods that utilize readily available resources deserves attention. Among the various forms of sign language, sign language and Arabic are especially significant, as Arabic is predominantly spoken in the Middle East and North Africa. Yet, the challenges that arise from the communication gap between this population and the hearing members of society continue to affect various areas of the deaf and hard-of-hearing individuals educational, social, and professional for educational, social, and, professional integration. Hence, creating automated systems that can comprehend Arabic Sign Language is an essential development in helping socially integrate and bridge communication gaps [1]. Materials and Methods: The proposed hand gesture recognition system relies on a multi-stage methodology aimed at high-resolution image processing and efficient feature extraction. The methodology begins with the data collection phase, where a set of images and videos representing letters and words in Arabic Sign Language is collected on the Kaggle Dataset. Results: The proposed Arabic Sign Language recognition system was developed through two principal stages: (1) segmentation using the HSV color space with experimentally defined thresholds, and (2) classification via a Convolutional Neural Network (CNN). The segmentation stage, relying on the HSV model with the defined lower range (H=0,S=30,V=60) and upper range (H=20,S=150,V=255), demonstrated effectiveness in isolating the hand region from complex backgrounds, contributed significantly to noise reduction and the removal of irrelevant small objects, and enhanced the clarity of the extracted hand region prior to classification. Conclusion: Recognizing sign languages ​​has always been a vital topic in societies, especially with the recent massive expansion of the deaf and mute community. Therefore, it is important to focus on easy and highly accurate techniques that can be relied upon with relatively available resources. The proposed methodology is highly flexible and scalable, as the HSV color space boundaries can be modified or the CNN architecture optimized to suit research requirements and the characteristics of different datasets.

ABSTRACT A set of conceptual metaphors of the mind in Japanese Sign Language (JSL) is explored from the perspectives of universality and cultural specificities, considering the influences of Eastern Asian and Deaf cultures. The image-schematic metaphors, ideas are objects and mind is a container, are common in JSL, similar to other spoken and sign languages (SLs). Conversely, the locations associated with mental activities in JSL are unique to the culture, with meanings, memory, and understanding being located beneath the surface, in the head, and around the chest, respectively. This differs from the structures reported in extant Western studies on SLs. Notably, the abdomen-centering conception of understanding is a cultural-specific feature that is shared by JSL and spoken Japanese. Additionally, the mind metaphors in JSL are not often shared with the ambient hearing culture. Our results reveal that (1) the metaphorical mapping from vision to cognition, as well as the use of visual-spatial representation, are specific to the metaphors in JSL and that (2) an etymological understanding of mental activity based on the experience of deaf people is manifested in the sign meaning, “to learn,” denoted by the action, “to steal the techniques by eyes.”

Sign language is a method of communication that enables deaf individuals to interact with others. In fictional media, sign language is frequently used to signal a character’s deafness to the audience. Many stories in fiction that showcase deaf characters utilise sign language as a primary method of communication. Sign language’s visual modality aligns closely with animation’s visual nature, making it a particularly effective medium for representing deaf communication. Beyond its functional role in conveying dialogue, signing within animation contributes to narrative richness by facilitating the exploration of interpersonal connections and shared linguistic identity. Japanese animation has increasingly featured deaf characters using sign language to foster communication and build emotional connections. The characters are depicted not only through the absence of a sensory capacity but also as social actors negotiating a world that frequently fails to understand or accommodate them. This paper examines the differences among the previously mentioned Japanese animations and analyses how they use sign language to communicate and convey emotional connections between characters.

Chilean Sign Language (LSCh) was recognized a few years ago as the official language of the deaf community, as well as an essential part of its culture and identity (Law 21.303, 2021). However, there are still no linguistic policies that guide, through an official curriculum, its teaching either as a first or second language. LSCh is taught in higher education contexts; however, there is no evidence of the methodologies that are most effective for its teaching. The present study analyzes the effectiveness of the teaching and learning methodologies implemented in an LSCh workshop for 20 second year students of Special Education. The methodological approach used is action research with a mixed design. The data collection strategies included classroom observations and filming, a focus group and a student survey. The results indicate that active methodologies, in particular, Game-Based Learning, Flipped Classroom and Cooperative Learning together prove to be successful for LSCh learning, promoting motivation, participation and course involvement. We recommend extending the study to other LSCh teaching contexts.

BackgroundThe paucity of data on the knowledge and attitudes of Deaf people towards dementia is well documented, as is the limited access to dementia services in this population due to lack of interpreters and absence of health professionals who use sign language (SL). Therefore, delivering dementia awareness in Deaf people is assumed as a high priority which is also related to the seeking of relevant diagnostic services. Additionally, till now there are almost no screening tools for detecting early dementia signs which are adapted in national SL.MethodTo address this gap, the current Erasmus+ project aims to identify attitudes and knowledge about dementia among the Deaf population using relevant psychometric tools and subsequently create dementia raising awareness seminars, tailored to the cultural background and norms of the Deaf communities in Austria, Germany, Greece, and Italy. Secondly, the only existing Cognitive Screening Test (CST), originally adapted in British SL from Atkinson et al. (2015) for detecting dementia in Deaf older adults will be also adapted in Austrian and Greek SL.ResultInitially, results from the tools measuring dementia attitudes and knowledge will be presented. Afterwards, the design of the structured seminars delivered in Deaf communities will be also displayed. Finally, the adapted versions of the Austrian and Greek CST administered in Deaf older adults in both countries will be available.ConclusionThe final goal is to increase dementia raising awareness seminars in Deaf people from Austria, Germany, Greece and Italy, as well as achieve dementia detection in this population by providing them with diagnostic and care pathways.

Reducing health inequalities in the deaf community: a priority setting exercise

Inclusive musical performances have been crucial in bridging the gap between hearing and Deaf communities, with sign language interpreters playing a vital role. However, these interpreters face significant challenges regarding musical expression and translation efficiency. We introduce a generative AI-based Sign Dance Maker (SDM) tool to address these issues. Informed by focus group interviews with four experienced music sign language interpreters, we designed SDM to integrate a fine-tuned dance generation model with a Large Language Model. This system enables text-based 3D motion editing, specifically supporting visual feedback and a streamlined translation process. A user study with 10 interpreters confirmed that SDM enhances creative expression while reducing cognitive burden in both qualitative and quantitative measures. Beyond performance applications, our findings suggest potential extensions to sign language education and improving accessibility in essential services, contributing to more inclusive cultural experiences through AI-assisted sign dance creation.

IntroductionApproximately one in seven individuals in the United States has hearing loss, yet many medical students have limited exposure to Deaf culture, accessibility practices, and health disparities faced by Deaf, DeafBlind, and Hard-of-Hearing (DDBHH) patients. To address this, an educational session was introduced that later expanded to include simulated scenarios.MethodsWe developed a two-part initiative incorporating community narratives, lecture, and case-based learning. First, we implemented a 2-hour elective interactive lecture co-designed with Deaf collaborators, which later expanded into a required session for third-year medical students. We developed a 2-hour simulation session featuring four scenarios with community-recruited and trained DDBHH patients, andvarious interpreters, including American Sign Language, Certified Deaf, Video Remote, and DeafBlind Interpreters. Presession and postsession surveys assessed student knowledge and confidence.ResultsAmong 236 third-year medical students, approximately 80% self-identified as having minimal or no knowledge of Deaf culture or disparities. Postlecture, the proportion of students who felt equipped to provide equitable care to DDBHH patients increased from 7% to 84% (35% response rate). Eight students participated in the simulated session. Postsimulation, knowledge scores increased by 75%, and mean confidence scores (5-point Likert scale; 1 = not confident, 5 = confident) in caring for Deaf and DeafBlind patients increased by 1.3 and 1.7 points, respectively (100% response rate). Both sessions received strong positive feedback that emphasized applicability of the experiences.DiscussionIntegrating education on DDBHH health disparities and culture into medical training is needed and feasible. Next steps include program expansion and supporting adaptation at other institutions.

Deep learning-based sign language recognition plays a pivotal role in facilitating communication for the deaf community. Current approaches, while effective, often introduce redundant information and incur excessive computational overhead through global feature interactions. To address these limitations, this paper introduces a Deformable Correlation Network (DCA) designed for efficient temporal modeling in continuous sign language recognition. The DCA integrates a Deformable Correlation (DC) module that leverages spatio-temporal driven offsets to adjust the sampling range adaptively, thereby minimizing interference. Additionally, a multi-scale local sampling strategy, guided by motion prior, enhances temporal modeling capability while reducing computational costs. Furthermore, an attention-based Correlation Matrix Filter (CMF) is proposed to suppress interference elements by accounting for feature motion patterns. A long-term temporal enhancement module, based on spatial aggregation, efficiently leverages global temporal information to model the performer’s holistic limb motion trajectories. Extensive experiments on three benchmark datasets demonstrate significant performance improvements, with a reduction in Word Error Rate (WER) of up to 7.0% on the CE-CSL dataset, showcasing the superiority and competitive advantage of the proposed DCA algorithm.

In Mexico, the Deaf community faces persistent communication barriers that restrict their integration and access to essential services, particularly in healthcare. Even though approximately two million individuals use Mexican Sign Language (MSL) as their primary form of communication, technological tools for supporting effective interaction remain limited. While recent research in sign-language recognition has led to important advances for several languages, work focused on MSL, particularly for healthcare scenarios, remains scarce. To address this gap, this study introduces a health-oriented dataset of 150 signs, with 800 synthetic video sequences per word, totaling more than 35 GB of data. This dataset was used to train recurrent neural networks with regularization and data augmentation. The best configuration achieved a maximum precision of 98.36% in isolated sign classification, minimizing false positives, which is an essential requirement in clinical applications. Beyond isolated recognition, the main contribution of this study is its exploratory evaluation of sequential narrative inference in MSL. Using short scripted narratives, the system achieved a global sequential recall of 45.45% under a realistic evaluation protocol that enforces temporal alignment. These results highlight both the potential of recurrent architectures in generalizing from isolated gestures to structured sequences and the substantial challenges posed by continuous signing, co-articulation, and signer-specific variation. While not intended for clinical deployment, the methodology, dataset, and open-source implementation presented here establish a reproducible baseline for future research. This work provides initial evidence, tools, and insights to support the long-term development of accessible technologies for the Deaf community in Mexico.

Sign language is a visual language that uses a variety of hand gesture combinations to express distinct ideas. It acts as a bridge to communicate with the outside world and is a vital communication tool for the deaf and mute community. Recent years have seen a fast increase in computer technology, opening up new avenues for research on sign language recognition through developments in computer graphics, computer vision, neural networks, and associated hardware. However, many sign language movements are still hard to tell apart because of the intrinsic constraints of visual component combinations. Natural language can be incorporated to help with the recognition process in order to overcome this difficulty. This review study highlights the benefits and drawbacks of modern approaches to sign language recognition in terms of recording, identifying, translating, and depicting motions. The primary difficulties in the field of sign language technology are also covered, along with the introduction of a number of useful applications. In order to encourage and support additional achievements in this field, future research directions and possible developments are finally suggested.

A qualitative study conducted in Denpasar revealed that adolescents with hearing disabilities possessed limited knowledge about reproductive health. This was attributed to the absence of specific topics addressing reproductive health within the school curriculum. Teachers did not explicitly deliver reproductive health education, and the school's health service unit was underutilized for this purpose. Additionally, the provision of reproductive health services was hindered by a lack of healthcare professionals proficient in sign language. Objectives: This community service aimed to increase the adolescent knowledge about reproductive health Method: The community service program provided reproductive health education to 25 adolescents with hearing disabilities utilizing the "Peer Educator Module for Reproductive Health and Premarital Counseling" developed by Udayana University's Public Health Study Program and the Ministry of Health's "Out-of-School Reproductive Health Module." We are using various methods and sign language interpretor in every session. Results: This community service program was conducted at the Alfatih Deaf Foundation, West Bandung Regency, Indonesia, from June to October 2024. It included eight sessions attended by 25 participants. Post-program assessments using questionnaires showed significant improvements in participants' understanding, demonstrated through accurate responses and enhanced analytical skills in addressing reproductive health and sexual violence prevention issues. Conclusion: The community service program on reproductive health education for deaf adolescents at the Alfatih Foundation was well-received, as reflected in high attendance rates and positive evaluation outcomes. The inclusion of sign language interpreters and interactive educational methods facilitated effective material delivery, resulting in improved reproductive health knowledge among participants. A key recommendation following this program is the establishment of peer educators for the deaf community.

This case study explores inclusive education through the experience of Daisy, a Deaf child in a New Zealand kindergarten, highlighting that true inclusion extends beyond access and into equity in experience and in outcome. Guided by the Aotearoa Deaf Community’s social model of disability, the study illustrates how kaiako, whānau, peers, and support services collaboratively removed barriers to participation. Using the five guiding questions aligned to Te Whāriki, the team creates a responsive environment where Daisy is empowered to flourish and thrive. This case provides early childhood educators with an example of a working model where theory has been successfully applied in practice to foster authentic inclusion, enabling Deaf children to experience belonging, connection, and equitable outcomes.

Created by and for the working class, Brazilian peripheral literature emerged as a means of expression for artists who inhabit and live in the periphery on a daily basis. Initiated by Ferréz with marginal literature and continued by Sérgio Vaz, then called peripheral literature, with the work of poetry slam collectives, the terrain of peripheral art was prepared for the germination of the poetry slam. Therefore, this text seeks to understand how the poetry slam arrived in Brazil, based on its relationship with peripheral literature, and how such literature shaped this Brazilian poetic movement. Being part of the great tree of spoken poetry, slam germinated and branched out, generating fruits, enabling a harvest in which peripheral literature is democratically accessed in public spaces. Women, the black population, the deaf community, indigenous peoples, the LGBTQIAPN+ community, and other subalternized subjects saw in slam poetry the opportunity to be their own references and the path to reclaiming their narratives.

Indonesian Sign Language (BISINDO) serves as a primary communication medium for the deaf community; however, limited public understanding often creates barriers during daily interactions. This study aims to develop a real-time BISINDO word-level translation system using hand landmark extraction and temporal modeling with Long Short-Term Memory (LSTM). The system employs MediaPipe Hands to detect 21 hand landmarks per frame, which are then processed as sequential motion patterns to classify five BISINDO words: saya, terima kasih, maaf, nama, and kamu. A total of 250 gesture samples were recorded under controlled lighting conditions as the primary dataset. The processed sequences were used to train the LSTM model, which was subsequently integrated with an ESP32 microcontroller and a DFPlayer Mini module to produce direct audio output. Experimental results show that the model achieved an average accuracy of 86%, with precision and recall values ranging from 0.81 to 0.94. The confusion matrix analysis indicates that most gestures were correctly classified, although some errors occurred in gestures with similar initial motion trajectories. Integration testing demonstrated an average system latency of 3.8 seconds and an audio output success rate of 85%. These findings indicate that the proposed system is capable of translating BISINDO word-level gestures accurately, responsively, and consistently in real-time conditions. This study provides a strong foundation for the broader development of sign language translation systems, with potential enhancements in vocabulary expansion, multi-user datasets, and hardware optimization for deployment in real-world environments.