Real-time Immersive human-computer interaction based on tracking and recognition of dynamic hand gestures

With fast developing and ever growing use of computer based technologies, human-computer interaction (HCI) plays an increasingly pivotal role. In virtual reality (VR), HCI technologies provide not only a better understanding of three-dimensional shapes and spaces, but also sensory immersion and physical interaction. With the hand based HCI being a key HCI modality for object manipulation and gesture based communication, challenges are presented to provide users a natural, intuitive, effortless, precise, and real-time method for HCI based on dynamic hand gestures, due to the complexity of hand postures formed by multiple joints with high degrees-of-freedom, the speed of hand movements with highly variable trajectories and rapid direction changes, and the precision required for interaction between hands and objects in the virtual world. Presented in this thesis is the design and development of a novel real-time HCI system based on a unique combination of a pair of data gloves based on fibre-optic curvature sensors to acquire finger joint angles, a hybrid tracking system based on inertia and ultrasound to capture hand position and orientation, and a stereoscopic display system to provide an immersive visual feedback. The potential and effectiveness of the proposed system is demonstrated through a number of applications, namely, hand gesture based virtual object manipulation and visualisation, hand gesture based direct sign writing, and hand gesture based finger spelling. For virtual object manipulation and visualisation, the system is shown to allow a user to select, translate, rotate, scale, release and visualise virtual objects (presented using graphics and volume data) in three-dimensional space using natural hand gestures in real-time. For direct sign writing, the system is shown to be able to display immediately the corresponding SignWriting symbols signed by a user using three different signing sequences and a range of complex hand gestures, which consist of various combinations of hand postures (with each finger open, half-bent, closed, adduction and abduction), eight hand orientations in horizontal/vertical plans, three palm facing directions, and various hand movements (which can have eight directions in horizontal/vertical plans, and can be repetitive, straight/curve, clockwise/anti-clockwise). The development includes a special visual interface to give not only a stereoscopic view of hand gestures and movements, but also a structured visual feedback for each stage of the signing sequence. An excellent basis is therefore formed to develop a full HCI based on all human gestures by integrating the proposed system with facial expression and body posture recognition methods. Furthermore, for finger spelling, the system is shown to be able to recognise five vowels signed by two hands using the British Sign Language in real-time

A critical investigation of deaf comprehension of signed tv news interpretation

This study investigates factors hampering comprehension of sign language interpretations rendered on South African TV news bulletins in terms of Deaf viewers’ expectancy norms and corpus analysis of authentic interpretations. The research fills a gap in the emerging discipline of Sign Language Interpreting Studies, specifically with reference to corpus studies. The study presents a new model for translation/interpretation evaluation based on the introduction of Grounded Theory (GT) into a reception-oriented model. The research question is addressed holistically in terms of target audience competencies and expectations, aspects of the physical setting, interpreters’ use of language and interpreting choices. The South African Deaf community are incorporated as experts into the assessment process, thereby empirically grounding the research within the socio-dynamic context of the target audience. Triangulation in data collection and analysis was provided by applying multiple mixed data collection methods, namely questionnaires, interviews, eye-tracking and corpus tools. The primary variables identified by the study are the small picture size and use of dialect. Secondary variables identified include inconsistent or inadequate use of non-manual features, incoherent or non-simultaneous mouthing, careless or incorrect sign execution, too fast signing, loss of visibility against skin or clothing, omission of vital elements of sentence structure, adherence to source language structures, meaningless additions, incorrect referencing, oversimplification and violations of Deaf norms of restructuring, information transfer, gatekeeping and third person interpreting. The identification of these factors allows the construction of a series of testable hypotheses, thereby providing a broad platform for further research. Apart from pioneering corpus-driven sign language interpreting research, the study makes significant contributions to present knowledge of evaluative models, interpreting strategies and norms and systems of transcription and annotation.Linguistics and Modern LanguagesThesis (D. Litt.et Phil.) (Linguistics