Search Results (6)

Purpose: Physical activity (PA) is vital for everyone’s health and wellbeing; however, there is, a paucity of research amongst culturally deaf adults. Especially, to understand the needs of deaf adults and how to get them involved in shaping interventions that would help deaf people to be physically active. The current study aimed to explore barriers and facilitators for engaging in PA amongst deaf adults. Method: Focus groups involving nine culturally deaf adults communicating using British sign language were conducted and analysed using reflexive thematic analysis. Findings: Barriers including physical barriers, lack of deaf spaces and deaf awareness, and a lack of personal motivations were identified. Enablers included group/social support, deaf-led activities and health and wellbeing awareness. The findings highlighted a strong deaf identity. Conclusions: Deaf adults face barriers due to spaces being made for hearing people, leading to feelings of social exclusion and a lack of spaces to engage in activity and socialise, despite being personally and socially motivated to engage in PA. Deaf identity should be considered when promoting PA to deaf adults. The current paper highlights research and practice implications regarding how to engage and work with deaf people to develop appropriate interventions. Full article

Human beings usually rely on communication to express their feeling and ideas and to solve disputes among themselves. A major component required for effective communication is language. Language can occur in different forms, including written symbols, gestures, and vocalizations. It is usually essential for all of the communicating parties to be fully conversant with a common language. However, to date this has not been the case between speech-impaired people who use sign language and people who use spoken languages. A number of different studies have pointed out a significant gaps between these two groups which can limit the ease of communication. Therefore, this study aims to develop an efficient deep learning model that can be used to predict British sign language in an attempt to narrow this communication gap between speech-impaired and non-speech-impaired people in the community. Two models were developed in this research, CNN and LSTM, and their performance was evaluated using a multi-class confusion matrix. The CNN model emerged with the highest performance, attaining training and testing accuracies of 98.8% and 97.4%, respectively. In addition, the model achieved average weighted precession and recall of 97% and 96%, respectively. On the other hand, the LSTM model’s performance was quite poor, with the maximum training and testing performance accuracies achieved being 49.4% and 48.7%, respectively. Our research concluded that the CNN model was the best for recognizing and determining British sign language. Full article

Parenting support services and programs develop and strengthen existing parenting skills. However, in the UK and despite the 2010 UK Equality Act’s provisions, these programs are generally not accessible for Deaf parents whose first and/or preferred language is British Sign Language (BSL) because the medium of instruction is typically spoken and written English. This small-scale qualitative interview study gauged North Walian Deaf parents’ needs and preferences for accessing parenting classes. A structured interview assessed a small group of North Walian Deaf parents’ language practices, their perceptions of parenting support and accessibility, and their needs and preferences when it comes to parenting classes. An additional case study of a Deaf parent’s experience of participating in an 11-week-long parenting course with an English-BSL interpreter provides further insight into how such classes can be made accessible to Deaf parents. The main interview findings were that the participants had substantially lower English skills than BSL skills, that face-to-face delivery was preferred over online BSL support, and that all materials should be made available in BSL. The case study further uncovered several small adjustments that should be made to face-to-face classes to make them accessible to Deaf parents. In conclusion, materials from already existing parenting classes should be translated into BSL, interpreters should be available, and small adjustments to face-to-face classes should be made, so that Deaf parents can access and participate in already existing parenting programs. Full article

A comprehensive analysis of Cypriot mosque architecture between the 19th and 21st centuries, from the Ottoman and British colonial periods to the present, does not exist. The phase after 1974, after the division of the island into a Turkish Cypriot, predominantly Muslim north and a Greek Cypriot, mainly Christian south, is especially insufficiently studied. This paper aims to interpret Cypriot mosque architecture and its meaning(s) through a comparative analysis, considering cultural, religious, and political developments. Based on an architectural survey and studies about Muslim Cypriot culture, this study investigates formal and spatial characteristics, focusing on the presence/absence of domed plan typologies and of minarets which, as visual symbolic markers, might express shifting cultural-religious notions and/or identities. Inconspicuous mosques without domes and minarets dominate until 1974. However, with the inter-communal tensions in the 1960s, the minaret possibly became a sign of Turkish identity, besides being a cultural-religious marker. This becomes more obvious after 1974 and is stressed by the (re)introduction of the dome. Since the late 1990s, an ostentatious and unprecedented neo-Ottoman architecture emphasizes visible and invisible meanings, and the Turkish presence in Cyprus stronger than before. The new architectural language visually underlines the influences from Turkey that North Cyprus has been experiencing. Full article

Many existing programs for countering violent extremism focus on either end of the radicalization spectrum. On one hand are prevention programs aimed at deterring individuals from starting down the path to violent extremism. On the other hand are disengagement/de-radicalization programs designed for assisting individuals who have been fully radicalized. Conspicuously absent are programs for those who fall in-between, into what might be referred to as the pre-criminal space: individuals who have begun to exhibit signs of radicalization, but for whom radicalization is not yet complete. The British Columbia Shift (BC Shift) initiative was created to assist individuals determined to be in this pre-criminal space; that is, those deemed to be in danger of radicalizing. The goal of BC Shift is to stop individuals from traveling further down the path of radicalization, and, ideally, to turn individuals away from the path. BC Shift operates as a navigational model, connecting at-risk individuals with services and supports in the community. BC Shift is a government initiative supported by the Canada Centre for Community Engagement and Prevention of Violence. It is a civilian organization that partners very closely with, but is separate from, law enforcement. In addition to its primary CRVE mandate, BC Shift has rapidly evolved and expanded into several other responsibilities, including coordination on national CVE standards; liaising with other CVE programs across Canada; maintaining stakeholder relationships; and helping create capacity through dialog and training. Although the program only began accepting referrals in 2019, its operation has already revealed many important lessons for CRVE programs. First, it is critically important to have the right people in the room. There has to be buy-in from the highest levels of partner agencies and stakeholders, particularly early on. Second, programs of this sort should leverage existing resources wherever possible. BC Shift has been lucky enough to coordinate with situation tables, such as the CHART program in Surrey. There are already many organizations doing excellent work in their respective communities; it is very helpful to plug into those resources. Third, even though BC Shift operates as a navigational hub, it has benefitted greatly from having a social worker as part of the team. This skill set is important in helping referred individuals feel comfortable with the process of accessing services and supports. Finally, marketing matters! CRVE programs such as BC Shift have to navigate a complex reality. The very concept of violent extremism is disconcerting to a lot of people in the community; these fears have to be addressed, and difficulties related to differences in perspective and language have to be overcome. BC Shift’s first year-and-a-half of operation has also highlighted several issues that have not yet been satisfactorily resolved. There is, for example, the “low hanging fruit” problem; agencies are typically referring less severe cases. Trying to get agencies to refer more serious cases has proved challenging. We hope that, by outlining these lessons and issues, this presentation proves to be useful to other CRVE initiatives. Full article

In this work, we show that a late fusion approach to multimodality in sign language recognition improves the overall ability of the model in comparison to the singular approaches of image classification (88.14%) and Leap Motion data classification (72.73%). With a large synchronous dataset of 18 BSL gestures collected from multiple subjects, two deep neural networks are benchmarked and compared to derive a best topology for each. The Vision model is implemented by a Convolutional Neural Network and optimised Artificial Neural Network, and the Leap Motion model is implemented by an evolutionary search of Artificial Neural Network topology. Next, the two best networks are fused for synchronised processing, which results in a better overall result (94.44%) as complementary features are learnt in addition to the original task. The hypothesis is further supported by application of the three models to a set of completely unseen data where a multimodality approach achieves the best results relative to the single sensor method. When transfer learning with the weights trained via British Sign Language, all three models outperform standard random weight distribution when classifying American Sign Language (ASL), and the best model overall for ASL classification was the transfer learning multimodality approach, which scored 82.55% accuracy. Full article