This study examines the relationship between gesture and a deep understanding of a second language. The participants, including the researcher, are second-language educators who have experience drastic changes in levels of fluency after switching from traditional teaching methods, prioritizing grammar and thematic teaching, to the gesture approach. Data of this phenomenon is collected through a series of semi-structured interviews giving priority to narrative accounts of personal experiences. A phenomenological framework is employed to allow the dialogues to fuse and new understandings to emerge in the spaces in between. The findings are presented in an in-depth conversation between the participants and including well-known dynamic systems theorists to allow new insights and connections to develop. which are then creatively summarized and further explored in the final chapter through multi-lingual slam poetry. / Graduate

Thesis (Ph. D.)--University of Adelaide, Dept. of Psychology, 1990. / Includes bibliographical references (leaves 288-308).

Thesis (M.F.A.)--Rochester Institute of Technology, 1984. / Typescript. Includes bibliographical references (leaf 26).

Issued also as Thesis (Ph. D.)--Columbia University. / "Bibliographical references to chapter I": p. 52-54.

Thesis--Berlin. / Cover title. Vita. "Die ganze Arbeit wird binnen Jahresfrist in Buchform erscheinen." Includes bibliographical references (p. 17-24).

Thesis (M.S.)--Missouri State University, 2009. / "May 2009." Includes bibliographical references (leaves 21-23). Also available online.

With the rapid growth of online devices, a new concept of Internet of Things (IoT) is emerging in which everyday devices will be connected to the Internet. As the number of devices in IoT is increasing, so is the complexity of the interactions between user and devices. There is a need to design intelligent user interfaces that could assist users in interactions. Many studies have been conducted on different interaction techniques such as proxemic and gesture interaction in order to propose an intuitive and intelligent system for controlling multimedia devices over the IoT, though most could not propose a universal solution. The present study proposes a proximity-based and gesture-enabled user interface for multimedia devices over IoT. The proposed method employs a cloud-based decision engine to support user to choose and interact with the most appropriate device, reliving the user from the burden of enumerating available devices manually. The decision engine observes the multimedia content and device properties, learns user preferences adaptively, and automatically recommends the most appropriate device to interact. In addition to that, the proposed system uses proximity information to find the user among people and provides her/him gesture control services. Furthermore, a new hand gesture vocabulary is proposed for controlling multimedia devices through conducting a multiphase elicitation study. The main advantage of this vocabulary is that it can be used for all multimedia devices. Both device recommendation system and gesture vocabulary are evaluated. The device recommendation system evaluation shows that the users agree with the proposed interaction 70% of the times. Moreover, the average agreement score of the proposed gesture vocabulary (0.56) exceeds the score of similar studies. An external user evaluation study shows that the average score of being a good-match is 4.08 out of 5 and the average of ease-of-performance equals to 4.21 out of 5. The memory test reveals that the proposed vocabulary is easy to remember since participants could remember and perform gestures in 3.13 seconds on average. In addition to that, the average accuracy of remembering gestures equals to 91.54%.

In this thesis, we propose and present a multimodal interaction system that can provide a natural way for human-computer interaction. The core idea of this system is to help users to interact with the machine naturally by recognizing various gestures from the user from a wearable device. To achieve this goal, we have implemented a system including both hardware solution and gesture recognizing approaches. For the hardware solution, we designed and implemented a data glove based interaction device with multiple kinds of sensors to detect finger formations, touch commands and hand postures. We also modified and implemented two gesture recognizing approach based on support vector machine (SVM) as well as the lookup table. The detailed design and information is presented in this thesis. In the end, the system achieves supporting over 30 kinds of touch commands, 18 kinds of finger formation, and 10 kinds of hand postures as well as the combination of finger formation and hand posture with the recognition rate of 86.67% as well as the accurate touch command detection. We also evaluated the system from the subjective user experience.

Humans engage in joint actions on a daily basis. Some of these joint actions are explicitly coordinated using, for example, speech and gesture, and the others are implicitly coordinated with the actions themselves. The first chapter of this dissertation reviews the use of speech, gesture, and intentional behavioral signals in explicit coordination of joint action and identifies three cognitive mechanisms that enable implicit coordination of joint action, namely, motor resonance, joint intentionality, and environmental and social affordance. The second chapter reports an empirical study exploring the employment of explicit and implicit coordination of joint action in a complex real-life joint activity, assembling a TV cart from its parts. We coded the content of the utterances and gestures that pairs of participants used throughout the assembly and the major and subordinate joint actions they performed. We then coded how each joint action was coordinated, that is, using speech, gestures, or action itself. The results showed speech and gesture served primarily to establish and sustain a shared mental model of the environmental affordances between the co-actors, which occurred primarily at the beginning of the task and as the participants began to attach two major parts. For both major and subordinate joint actions alike, the specifics of the joint actions such as the goal and division of labor was primarily coordinated implicitly. We argue that the shared mental model scaffolded the participants’ implicit coordination of the actions. These findings provide evidence that action itself is a communicative device and part of the conversation between co-actors of a joint activity. They also lend support to the argument that joint action cannot be fully understood on the individual level but must be interpreted as a collective of which each individual is a part.

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