Doctor Pharmaceuticae - DPharm / South Africa's White Paper for the transformation of the health care system in South Africa (DOH, 2007) acknowledges major disparities and inequalities as a result of an imprint by apartheid policies. In its transition to democracy, health promotion strategies have been initiated to address these disparities. However, such strategies have been narrowed and "favoured target audiences that are literate, urban-based and who have easy access to print and audio-visual media" (DOH, 1997). This implies that many vulnerable and marginalised groupings in South Africa, including the Deaf community are excluded from health promotion endeavours. Deaf people in South Africa communicate using South African Sign Language (SASL) and majority of the Deaf community exhibit poor literacy levels. Deafness is a significant communication barrier which limits a Deaf person's prospect to attain the best possible health care (Barnett, et al 2011). Various means of communication including spoken language, written instructions and the use of pictograms are used by healthcare workers to communicate health-related information. For many members of the Deaf community who communicate primarily in sign language, these methods are a sub-standard and prevent the attainment of optimum therapeutic outcomes. With regard to pharmaco-therapeutic services, Deaf people cannot hear the spoken language used by pharmacists during patient counselling, and their compromised functional literacy hinders the ability to read instructions on medicine labels. With both the spoken and written means of communication compromised, the Deaf patient's ability to comprehend instruction by pharmacists on how to use their medicines is inadequate and as a result, a Deaf patient may leave the pharmacy with medicine, but a poor understanding of how to use the medicine safely and effectively. Previous researchers have worked on building a technology base, including industrial design and computer science expertise to conceptualize the groundwork of a mobile phone application called SignSupport to facilitate communication between medical doctors and Deaf individuals. The particulars of the pharmacy scenario however, require a pharmacy-specific device to be of use in the dispensing of medicines to a Deaf patient in a pharmacy. The over-arching goal of this thesis is to design and evaluate a mobile phone application to facilitate the communication of medicine instructions between a Deaf patient and a pharmacist. Qualitative, participatory action research and community-based co-design strategies were directed toward Deaf participants, senior pharmacy students and pharmacists to create a prototype of the afore-mentioned mobile phone application. Preliminary results indicated that the application was suitable to pharmacists and Deaf community. Furthermore, both sets of users approved the overall design and were receptive to and keen on the practical uses of the application. Inadequacies pointed out by the Deaf community and pharmacists were addressed as an iterative modification to the prototype and culminated in version 2 which was deployed in an actual hospital pharmacy in 2015. Hospital usability studies generated largely positive results from both Deaf users and pharmacists, indicating that SignSupport is able to facilitate communication between pharmacists and Deaf patients. Next steps include advancing the application to a market–ready version that is downloadable and available as an application on the play stores of commercially available smart phones. / National Research Foundation

Masters of Science / Much work has been done in building systems that can recognise gestures, e.g. as a component of sign language recognition systems. These systems typically use whole gestures as the smallest unit for recognition. Although high recognition rates have been reported, these systems do not scale well and are computationally intensive. The reason why these systems generally scale poorly is that they recognize gestures by building individual models for each separate gesture; as the number of gestures grows, so does the required number of models. Beyond a certain threshold number of gestures to be recognized, this approach becomes infeasible. This work proposes that similarly good recognition rates can be achieved by building models for subcomponents of whole gestures, so-called cheremes. Instead of building models for entire gestures, we build models for cheremes and recognize gestures as sequences of such cheremes. The assumption is that many gestures share cheremes and that the number of cheremes necessary to describe gestures is much smaller than the number of gestures. This small number of cheremes then makes it possible to recognize a large number of gestures with a small number of chereme models. This approach is akin to phoneme-based speech recognition systems where utterances are recognized as phonemes which in turn are combined into words. We attempt to recognise and classify cheremes found in South African Sign Language (SASL). We introduce a method for the automatic discovery of cheremes in dynamic signs. We design, train and use hidden Markov models (HMMs) for chereme recognition. Our results show that this approach is feasible in that it not only scales well, but it also generalizes well. We are able to recognize cheremes in signs that were not used for training HMMs; this generalization ability is a basic necessity for chemere-based gesture recognition. Our approach can thus lay the foundation for building a SASL dynamic gesture recognition system.

Research conducted on the prevalence of behavioural adjustment in Deaf children and adolescents, in erstwhile countries, points towards an appreciably elevated percentage of emotional and behavioural problems amongst this population group when compared to hearing normative groups. Studies specify that the prevalence of behaviour and emotional problems in Deaf children and adolescents varies from 4.8% to 50.3%. From existing research conducted, it is ambiguous as to why the reported prevalence rates of maladjustment are higher amongst Deaf children and adolescents. This pioneering study is the first of its kind to research dissimilar modalities of language used as the language of learning and teaching (LoLT) in schools for Deaf learners and how this could possibly correlate to learner behaviour in the classroom. Taking into consideration the reported pervasiveness of maladjustment in Deaf children and adolescents; this study uses the Teacher Report Form (TRF) to investigate the types of behaviour problems displayed by Deaf learners in the classroom. It further investigates whether Deaf learners display certain types of behaviour problems when dissimilar modalities of language are used as the language of learning and teaching. The overall findings of this study suggest that teachers who use manually coded spoken language report an elevated prevalence of behaviour problems on the TRF compared to teachers who use South African Sign Language (SASL). Results further suggest that the group of teachers who use SASL report somatic complaints and attention problems as the most frequently encountered behaviour problems in their classrooms. In comparison the group of teachers who use manually coded spoken English (MCE) report social problems and attention problems as the most frequently encountered behaviour problems in their classrooms. Limitations of this study and suggestions for future research are discussed.

>Magister Scientiae - MSc / This thesis presents a system for performing whole gesture recognition for South African Sign Language. The system uses feature vectors combined with Hidden Markov models. In order to construct a feature vector, dynamic segmentation must occur to extract the signer's hand movements. Techniques and methods for normalising variations that occur when recording a signer performing a gesture, are investigated. The system has a classification rate of 69%.