While recognizing some kinds of human motion patterns requires detailed feature representation and tracking, many of them can be recognized using global features. The global configuration or structure of an object in a frame can be expressed as a probability density function constructed using relational attributes between low-level features, e.g. edge pixels that are extracted from the regions of interest. The probability density changes with motion, tracing a trajectory in the latent space of distributions, which we call the configuration space. These trajectories can then be used for recognition using standard techniques such as dynamic time warping. Can these frame-wise probability functions, which usually have high dimensionality, be embedded into a low-dimensional space so that we can still estimate various meaningful probabilistic distances in the new space? Given these trajectory-based representations, can one learn models of signs in an unsupervised manner? We address these two fundamental questions in this dissertation. Existing embedding approaches do not extend easily to preserve meaningful probabilistic distances between the samples. We present an embedding framework to preserve the probabilistic distances like Chernoff, Bhattacharya, Matusita, KL or symmetric-KL based on dot-products between points in this space. It results in computational savings. We experiment with the five different probabilistic distance measures and show the usefulness of the representation in three different contexts - sign recognition of 147 different signs (with large number of possible classes), gesture recognition with 7 different gestures performed by 7 different persons (with person variations) and classification of 8 different kinds of human-human interaction sequences (with segmentation problems). Currently, researchers in continuous sign language recognition assume that the training signs are already available and often those are manually selected from continuous sentences. It consumes a lot of human time and is tedious. We present an approach for automatically learning signs from multiple sentences by using a probabilistic framework to extract the parts of signs that are present in most of its occurrences, and are robust to variations produced by adjacent signs. We show results by learning 10 signs and 10 spoken words from 136 sign language sentences and 136 spoken sequences respectively.

abstract: The purpose of the research conducted and presented in this thesis is to explore mentoring programs for ASL/English Interpreters, with a focus on the question "Is a Peer Mentoring Program a successful approach to mentoring working and novice interpreter?" The method of qualitative data collection was done via questionnaires and interviews with past participants of a Peer Mentoring Program and questionnaires to identified persons who have experience creating and running mentoring programs. The results of the data collection show that a Peer Mentoring Program is a successful approach to mentoring working and novice interpreters. This research provides valued information in regard to the experience of persons in a Peer Mentoring Program as well as successful aspects of such a mentoring approach. / Dissertation/Thesis / M.A. Social and Philosophical Foundations of Education 2012

This study examined the effectiveness of two procedures, a visual cue and performance posting, to modify the use of sign language by psychiatric technicians. The visual cue was first introduced alone, then paired with performance posting to encourage staff use of sign language with the developmentally disabled children in their charge. Application of the visual cue alone produced little change in staff sign useage. The visual cue plus performance posting condition increased staff use of sign language during mealtimes. Four weeks of follow-up data indicated that the use of sign language remained at a level well above baseline.

Appropriate digital citizenship skills are considered essential for modern professionals, including signed language interpreters. However, little is known about the experiences and practices of interpreter educators regarding digital citizenship. This exploratory qualitative interview study was conducted to examine the experiences and practices of interpreter educators related to incorporating opportunities for digital citizenship skill-building in their teaching practice. A conceptual framework based on digital citizenship theory guided development of this study. Data were collected from interviews of 6 interpreter educators in bachelor-degree programs in American Sign Language/English interpreting across the United States. Data sets were analyzed through open and axial coding and assessed for themes and patterns. Findings of the study indicated that interpreter educators were aware of elements of digital citizenship but were not knowledgeable about institutional or other policies, that they prioritized the soft skills of digital citizenship, and that they assumed their students acquired the technical skills of digital citizenship elsewhere. Findings may lead to better informed pedagogical decisions about incorporating digital citizenship into instruction, better prepared new professionals, and can contribute to positive social change for practitioners and the consumers they serve.

abstract: This dissertation examines the first impressions that occur between Deaf consumers and American Sign Language (ASL)/English interpreters prior to a healthcare appointment. Negative first impressions can lead to a disconnect or loss of trust between Deaf consumers and interpreters and increase the risk for Deaf consumers to receive inadequate healthcare. The recognition of this risk led to an action research study to look at barriers to successful interactions between ASL/English interpreters and Deaf consumers. The mixed methods research design and associated research questions discovered factors and perceptions that contributed to the disconnect and subsequently informed a 10-week intervention with a small group of ASL/English interpreters and Deaf consumers. The factors that influence connection are system related and a lack of a standardized approach to using name badges, missing or incorrect appointment details, and an inconsistent protocol for interpreter behavior when a healthcare provider leaves the room. The intervention allowed the interpreter participants to generate solutions to mitigate these barriers to connection and apply them during the 10 weeks. Deaf consumer feedback was gathered during the intervention period and was used to modify the generated solutions. The generated solutions included re-design of an interpreter referral agency’s name badge, using small talk as a way to learn information about the nature of the healthcare appointment and proactively discuss procedures when a healthcare provider leaves the exam room. These solutions resulted in a positive influence for both interpreters and Deaf consumers and an increase of trust and connection. The findings of this study show new approaches that create a connection between interpreters and Deaf consumers and may lead to more satisfactory healthcare interactions for Deaf consumers. / Dissertation/Thesis / Doctoral Dissertation Leadership and Innovation 2019

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The purpose of the thesis was to create a data glove that can translate ASL by reading the finger- and hand movements. Furthermore, the applicability of conductive fabric as stretch sensors was explored. To read the hand gestures stretch sensors constructed from conductive fabric were attached to each finger of the glove to distinguish how much they were bent. The hand movements were registered using a 3-axis accelerometer which was mounted on the glove. The sensor values were read by an Arduino Nano 33 IoT mounted to the wrist of the glove which processed the readings and translated them into the corresponding sign. The microcontroller would then wirelessly transmit the result to another device through Bluetooth Low Energy. The glove was able to correctly translate all the signs of the ASL alphabet with an average accuracy of 93%. It was found that signs with small differences in hand gestures such as S and T were harder to distinguish between which would result in an accuracy of 70% for these specific signs. / Syftet med uppsatsen var att skapa en datahandske som kan översätta ASL genom att läsa av finger- och handrörelser. Vidare undersöktes om ledande tyg kan användas som sträcksensorer. För att läsa av handgesterna fästes ledande tyg på varje finger på handsken för att urskilja hur mycket de böjdes. Handrörelserna registrerades med en 3-axlig accelerometer som var monterad på handsken. Sensorvärdena lästes av en Arduino Nano 33 IoT monterad på handleden som översatte till de motsvarande tecknen. Mikrokontrollern överförde sedan resultatet trådlöst till en annan enhet via Bluetooth Low Energy. Handsken kunde korrekt översätta alla tecken på ASL-alfabetet med en genomsnittlig exakthet på 93%. Det visade sig att tecken med små skillnader i handgester som S och T var svårare att skilja mellan vilket resulterade i en noggrannhet på 70% för dessa specifika tecken.