Developing an interactive overview for non-visual exploration of tabular numerical information

This thesis investigates the problem of obtaining overview information from complex tabular numerical data sets non-visually. Blind and visually impaired people need to access and analyse numerical data, both in education and in professional occupations. Obtaining an overview is a necessary first step in data analysis, for which current non-visual data accessibility methods offer little support. This thesis describes a new interactive parametric sonification technique called High-Density Sonification (HDS), which facilitates the process of extracting overview information from the data easily and efficiently by rendering multiple data points as single auditory events. Beyond obtaining an overview of the data, experimental studies showed that the capabilities of human auditory perception and cognition to extract meaning from HDS representations could be used to reliably estimate relative arithmetic mean values within large tabular data sets. Following a user-centred design methodology, HDS was implemented as the primary form of overview information display in a multimodal interface called TableVis. This interface supports the active process of interactive data exploration non-visually, making use of proprioception to maintain contextual information during exploration (non-visual focus+context), vibrotactile data annotations (EMA-Tactons) that can be used as external memory aids to prevent high mental workload levels, and speech synthesis to access detailed information on demand. A series of empirical studies was conducted to quantify the performance attained in the exploration of tabular data sets for overview information using TableVis. This was done by comparing HDS with the main current non-visual accessibility technique (speech synthesis), and by quantifying the effect of different sizes of data sets on user performance, which showed that HDS resulted in better performance than speech, and that this performance was not heavily dependent on the size of the data set. In addition, levels of subjective workload during exploration tasks using TableVis were investigated, resulting in the proposal of EMA-Tactons, vibrotactile annotations that the user can add to the data in order to prevent working memory saturation in the most demanding data exploration scenarios. An experimental evaluation found that EMA-Tactons significantly reduced mental workload in data exploration tasks. Thus, the work described in this thesis provides a basis for the interactive non-visual exploration of a broad range of sizes of numerical data tables by offering techniques to extract overview information quickly, performing perceptual estimations of data descriptors (relative arithmetic mean) and managing demands on mental workload through vibrotactile data annotations, while seamlessly linking with explorations at different levels of detail and preserving spatial data representation metaphors to support collaboration with sighted users

Visuo-Haptic Exploration for Multimodal Memory

When faced with a novel object, we explore it to understand its shape. This way we combine information coming from different senses, as touch, proprioception and vision, together with the motor information embedded in our motor execution plan. The exploration process provides a structure and constrains this rich flow of inputs, supporting the formation of a unified percept and the memorization of the object features. However, how the exploration strategies are planned is still an open question. In particular, is the exploration strategy used to memorize an object different from the exploration strategy adopted in a recall task? To address this question we used iCube, a sensorized cube which measures its orientation in space and the location of the contacts on its faces. Participants were required to explore the cube faces where little pins were positioned in varying number. Participants had to explore the cube twice and individuate potential differences between the two presentations, which could be performed either haptically alone, or with also vision available. The haptic and visuo-haptic (VH) exploratory strategies changed significantly when finalized to memorize the structure of the object with respect to when the same object was explored to recall and compare it with its memorized instance. These findings indicate that exploratory strategies are adapted not only to the property of the object to be analyzed but also to the prospective use of the resulting representation, be it memorization or recall. The results are discussed in light of the possibility of a systematic modeling of natural VH exploration strategies

Continually improving grounded natural language understanding through human-robot dialog

As robots become ubiquitous in homes and workplaces such as hospitals and factories, they must be able to communicate with humans. Several kinds of knowledge are required to understand and respond to a human's natural language commands and questions. If a person requests an assistant robot to take me to Alice's office, the robot must know that Alice is a person who owns some unique office, and that take me means it should navigate there. Similarly, if a person requests bring me the heavy, green mug, the robot must have accurate mental models of the physical concepts heavy, green, and mug. To avoid forcing humans to use key phrases or words robots already know, this thesis focuses on helping robots understanding new language constructs through interactions with humans and with the world around them. To understand a command in natural language, a robot must first convert that command to an internal representation that it can reason with. Semantic parsing is a method for performing this conversion, and the target representation is often semantic forms represented as predicate logic with lambda calculus. Traditional semantic parsing relies on hand-crafted resources from a human expert: an ontology of concepts, a lexicon connecting language to those concepts, and training examples of language with abstract meanings. One thrust of this thesis is to perform semantic parsing with sparse initial data. We use the conversations between a robot and human users to induce pairs of natural language utterances with the target semantic forms a robot discovers through its questions, reducing the annotation effort of creating training examples for parsing. We use this data to build more dialog-capable robots in new domains with much less expert human effort (Thomason et al., 2015; Padmakumar et al., 2017). Meanings of many language concepts are bound to the physical world. Understanding object properties and categories, such as heavy, green, and mug requires interacting with and perceiving the physical world. Embodied robots can use manipulation capabilities, such as pushing, picking up, and dropping objects to gather sensory data about them. This data can be used to understand non-visual concepts like heavy and empty (e.g. get the empty carton of milk from the fridge), and assist with concepts that have both visual and non-visual expression (e.g. tall things look big and also exert force sooner than short things when pressed down on). A second thrust of this thesis focuses on strategies for learning these concepts using multi-modal sensory information. We use human-in-the-loop learning to get labels between concept words and actual objects in the environment (Thomason et al., 2016, 2017). We also explore ways to tease out polysemy and synonymy in concept words (Thomason and Mooney, 2017) such as light, which can refer to a weight or a color, the latter sense being synonymous with pale. Additionally, pushing, picking up, and dropping objects to gather sensory information is prohibitively time-consuming, so we investigate strategies for using linguistic information and human input to expedite exploration when learning a new concept (Thomason et al., 2018). Finally, we build an integrated agent with both parsing and perception capabilities that learns from conversations with users to improve both components over time. We demonstrate that parser learning from conversations (Thomason et al., 2015) can be combined with multi-modal perception (Thomason et al., 2016) using predicate-object labels gathered through opportunistic active learning (Thomason et al., 2017) during those conversations to improve performance for understanding natural language commands from humans. Human users also qualitatively rate this integrated learning agent as more usable after it has improved from conversation-based learning.Computer Science

Multisensory learning in adaptive interactive systems

The main purpose of my work is to investigate multisensory perceptual learning and sensory integration in the design and development of adaptive user interfaces for educational purposes. To this aim, starting from renewed understanding from neuroscience and cognitive science on multisensory perceptual learning and sensory integration, I developed a theoretical computational model for designing multimodal learning technologies that take into account these results. Main theoretical foundations of my research are multisensory perceptual learning theories and the research on sensory processing and integration, embodied cognition theories, computational models of non-verbal and emotion communication in full-body movement, and human-computer interaction models. Finally, a computational model was applied in two case studies, based on two EU ICT-H2020 Projects, "weDRAW" and "TELMI", on which I worked during the PhD

Wine communication in a global market: a study of metaphor through the genre of Australian wine reviews

This thesis is a report on wine communication focused on metaphoric language identified in the genre of wine reviews. Specifically, the research centred on Australian wine reviews written by Australian wine critics about Australian wines currently exported to the greater China region. In the genre of wine reviews, metaphoric expressions are frequently used to talk about wine (Caballero & Suárez-Toste, 2008). The thesis developed understanding of the influence of metaphoric language and its potential to constrain or motivate people’s sensory and affective responses to wine and highlighted the need to consider congruency of metaphoric language in terms of wine communication and education. The research was theoretically framed by the conceptual metaphor theory (CMT) of Lakoff and Johnson (1980) and took a cognitive linguistic perspective to metaphor analysis (Croft & Cruse, 2004). Wine appreciation was argued to be a social event in contrast to an observational event. From this perspective, wine appreciation is concerned with influencing audience perceptions in contrast to a spontaneous commentary of an event. The thesis presents the findings of two qualitative studies that used a corpus approach to metaphor use and understanding in the genre of wine reviews. The investigation identified metaphoric expressions in Australian wine reviews and went on to explore their understanding and transfer by wine educators in Australia and China. Metaphor identification used the Metaphor Identification Procedure Vrije Universiteit (Steen et al., 2010) and the UCREL Semantic Annotation System (Archer et al., 2004) for semantic and conceptual analysis. Results indicated six underpinning metaphoric themes (i.e., AN OBJECT, A THREE DIMENSIONAL ARTEFACT, AN INSTITUTIONAL ARTEFACT, A TEXTILE, A LIVING ORGANISM, and A PERSON) of which spatial and temporal properties were often integrated. A comparison of wine educator responses to interpretation and transmission tasks showed that anthropomorphic metaphor (i.e., WINE IS A PERSON) tended to be conceptualized similarly by participants more often than other metaphoric themes. In conclusion, the cultural artefact of language used in the genre of wine reviews and the metaphoric potential of linguistic choices on sensory and affective perceptions indicates a need for the consideration of congruency when wine communication crosses cultural and linguistic borders

Principles and Guidelines for Advancement of Touchscreen-Based Non-visual Access to 2D Spatial Information

Graphical materials such as graphs and maps are often inaccessible to millions of blind and visually-impaired (BVI) people, which negatively impacts their educational prospects, ability to travel, and vocational opportunities. To address this longstanding issue, a three-phase research program was conducted that builds on and extends previous work establishing touchscreen-based haptic cuing as a viable alternative for conveying digital graphics to BVI users. Although promising, this approach poses unique challenges that can only be addressed by schematizing the underlying graphical information based on perceptual and spatio-cognitive characteristics pertinent to touchscreen-based haptic access. Towards this end, this dissertation empirically identified a set of design parameters and guidelines through a logical progression of seven experiments. Phase I investigated perceptual characteristics related to touchscreen-based graphical access using vibrotactile stimuli, with results establishing three core perceptual guidelines: (1) a minimum line width of 1mm should be maintained for accurate line-detection (Exp-1), (2) a minimum interline gap of 4mm should be used for accurate discrimination of parallel vibrotactile lines (Exp-2), and (3) a minimum angular separation of 4mm should be used for accurate discrimination of oriented vibrotactile lines (Exp-3). Building on these parameters, Phase II studied the core spatio-cognitive characteristics pertinent to touchscreen-based non-visual learning of graphical information, with results leading to the specification of three design guidelines: (1) a minimum width of 4mm should be used for supporting tasks that require tracing of vibrotactile lines and judging their orientation (Exp-4), (2) a minimum width of 4mm should be maintained for accurate line tracing and learning of complex spatial path patterns (Exp-5), and (3) vibrotactile feedback should be used as a guiding cue to support the most accurate line tracing performance (Exp-6). Finally, Phase III demonstrated that schematizing line-based maps based on these design guidelines leads to development of an accurate cognitive map. Results from Experiment-7 provide theoretical evidence in support of learning from vision and touch as leading to the development of functionally equivalent amodal spatial representations in memory. Findings from all seven experiments contribute to new theories of haptic information processing that can guide the development of new touchscreen-based non-visual graphical access solutions

Socially intelligent robots that understand and respond to human touch

Touch is an important nonverbal form of interpersonal interaction which is used to communicate emotions and other social messages. As interactions with social robots are likely to become more common in the near future these robots should also be able to engage in tactile interaction with humans. Therefore, the aim of the research presented in this dissertation is to work towards socially intelligent robots that can understand and respond to human touch. To become a socially intelligent actor a robot must be able to sense, classify and interpret human touch and respond to this in an appropriate manner. To this end we present work that addresses different parts of this interaction cycle. The contributions of this dissertation are the following. We have made a touch gesture dataset available to the research community and have presented benchmark results. Furthermore, we have sparked interest into the new field of social touch recognition by organizing a machine learning challenge and have pinpointed directions for further research. Also, we have exposed potential difficulties for the recognition of social touch in more naturalistic settings. Moreover, the findings presented in this dissertation can help to inform the design of a behavioral model for robot pet companions that can understand and respond to human touch. Additionally, we have focused on the requirements for tactile interaction with robot pets for health care applications

The development of a set of principles for the through-life management of engineering information

Belgium Herbarium image of Meise Botanic Garden

Pinching sweaters on your phone – iShoogle : multi-gesture touchscreen fabric simulator using natural on-fabric gestures to communicate textile qualities

The inability to touch fabrics online frustrates consumers, who are used to evaluating physical textiles by engaging in complex, natural gestural interactions. When customers interact with physical fabrics, they combine cross-modal information about the fabric's look, sound and handle to build an impression of its physical qualities. But whenever an interaction with a fabric is limited (i.e. when watching clothes online) there is a perceptual gap between the fabric qualities perceived digitally and the actual fabric qualities that a person would perceive when interacting with the physical fabric. The goal of this thesis was to create a fabric simulator that minimized this perceptual gap, enabling accurate perception of the qualities of fabrics presented digitally. We designed iShoogle, a multi-gesture touch-screen sound-enabled fabric simulator that aimed to create an accurate representation of fabric qualities without the need for touching the physical fabric swatch. iShoogle uses on-screen gestures (inspired by natural on-fabric movements e.g. Crunching) to control pre-recorded videos and audio of fabrics being deformed (e.g. being Crunched). iShoogle creates an illusion of direct video manipulation and also direct manipulation of the displayed fabric. This thesis describes the results of nine studies leading towards the development and evaluation of iShoogle. In the first three studies, we combined expert and non-expert textile-descriptive words and grouped them into eight dimensions labelled with terms Crisp, Hard, Soft, Textured, Flexible, Furry, Rough and Smooth. These terms were used to rate fabric qualities throughout the thesis. We observed natural on-fabric gestures during a fabric handling study (Study 4) and used the results to design iShoogle's on-screen gestures. In Study 5 we examined iShoogle's performance and speed in a fabric handling task and in Study 6 we investigated users' preferences for sound playback interactivity. iShoogle's accuracy was then evaluated in the last three studies by comparing participants’ ratings of textile qualities when using iShoogle with ratings produced when handling physical swatches. We also described the recording and processing techniques for the video and audio content that iShoogle used. Finally, we described the iShoogle iPhone app that was released to the general public. Our evaluation studies showed that iShoogle significantly improved the accuracy of fabric perception in at least some cases. Further research could investigate which fabric qualities and which fabrics are particularly suited to be represented with iShoogle