Phygital interfaces for people with intellectual disability: an exploratory study at a social care center

Phygital interaction is a form of tangible interaction where digital and physical contents are combined in such a way that the locus of multimedia information is detached from the physical material(s) manipulated by the user. The use of phygital interaction is supported by several theoretical approaches that emphasize the development of cognitive skills dependent upon embodied interactions with the physical environment. Several studies demonstrate the potential of using phygital technologies for supporting people with intellectual disabilities (ID) in the development of cognitive, sensorimotor, social and behavioral skills. Our research aims at exploring the potential of phygital interaction for (young) adults with ID in a real setting, using a research platform called Reflex as a case study. For this purpose, we ran an empirical study involving 17 participants with ID and 8 specialists, and compared Reflex with approaches making use of only digital contents or paper-based materials. Our findings highlighted the potentials of phygital approaches to perform interventions with people with ID, enhancing their performances with an appreciated interaction method. In addition, the post-study interviews with specialists favoured the adoption of phygital technologies in a social care context.

The Robot is Present: Creative Approaches for Artistic Expression With Robots

There is growing interest in developing creative applications for robots, specifically robots that provide entertainment, companionship, or motivation. Identifying the hallmarks of human creativity and discerning how these processes might be replicated or assisted by robots remain open questions. Transdisciplinary collaborations between artists and engineers can offer insights into how robots might foster creativity for human artists and open up new pathways for designing interactive systems. This paper presents an exploratory research project centered on drawing with robots. Using an arts-led, practice-based methodology, we developed custom hardware and software tools to support collaborative drawing with an industrial robot. A team of artists and engineers collaborated over a 6-month period to investigate the creative potential of collaborative drawing with a robot. The exploratory project focused on identifying creative and collaborative processes in the visual arts, and later on developing tools and features that would allow robots to participate meaningfully in these processes. The outcomes include a custom interface for controlling and programming robot motion (EMCAR) and custom tools for replicating experimental techniques used in visual art. We report on the artistic and technical outcomes and identify key features of process-led (as opposed to outcome-led) approaches for designing collaborative and creative systems. We also consider the value of embodied and tangible interaction for artists working collaboratively with computational systems. Transdisciplinary research can help researchers uncover new approaches for designing interfaces for interacting with machines.

Tactowel: A Subtle Sports Performance Display for Giving Real-Time Performance Feedback in Tennis

Sports technology enhances athletes’ performance by providing feedback. However, interaction techniques of current devices may overwhelm athletes with excessive information or distract them from their performance. Despite previous research, design knowledge on how to interact with these devices to prevent such occasions are scarce. To address this gap, we introduce subtle displays as real-time sports performance feedback output devices that unobtrusively present low-resolution information. In this paper, we conceptualize and apply subtle displays to tennis by designing Tactowel, a texture changing sports towel. We evaluate Tactowel through a remote user study with 8 professional tennis players, in which they experience, compare and discuss Tactowel. Our results suggest subtle displays could prevent overwhelming and distracting athletes through three distinct design strategies: (1) Restricting the use excluding duration of performance, (2) using the available routines and interactions, and (3) giving an overall abstraction through tangible interaction. We discuss these results to present design implications and future considerations for designing subtle displays.

Tangible visual analytics: the integration of tangible interactions and computational techniques for biological data visualization and modelling with experts in the loop

Understanding and interpreting the inherently uncertain nature of complex biological systems, as well as the time to an event in these systems, are notable challenges in the field of bioinformatics. Overcoming these challenges could potentially lead to scientific discoveries, for example paving the path for the design of new drugs to target specific diseases such as cancer, or helping to apply more effective treatment for these diseases. In general, reverse engineering of these types of biological systems using online datasets is difficult. In particular, finding a unique solution to these systems is hard due to their complexity and the small sample size of datasets. This remains an unsolved problem due to such uncertainty, and the often intractable solution space of these systems. The term"uncertainty" describes the application-based margin of significance, validity, and efficiency of inferred or predictive models in their ability to extract characteristic properties and features describing the observed state of a given biological system. In this work, uncertainties within two specific bioinformatics domains are considered, namely "gene regulatory network reconstruction" (in which gene interactions/relationships within a biological entity are inferred from gene expression data); and "cancer survivorship prediction" (in which patient survival rates are predicted based on clinical factors and treatment outcomes). One approach to reduce uncertainty is to apply different constraints that have particular relevance to each application domain. In gene network reconstruction for instance, the consideration of constraints such as sparsity, stability and modularity, can inform and reduce uncertainty in the inferred reconstructions. While in cancer survival prediction, there is uncertainty in determining which clinical features (or feature aggregates) can improve associated prediction models. The inherent lack of understanding of how, why and when such constraints should be applied, however, prompts the need for a radically new approach. In this dissertation, a new approach is thus considered to aid human expert users in understanding and exploring inherent uncertainties associated with these two bioinformatics domains. Specifically, a novel set of tools is introduced and developed to assist in evidence gathering, constraint definition, and refinement of models toward the discovery of better solutions. This dissertation employs computational approaches, including convex optimization and feature selection/aggregation, in order to increase the chances of finding a unique solution. These approaches are realized through three novel interactive tools that employ tangible interaction in combination with graphical visualization to enable experts to query and manipulate the data. Tangible interaction provides physical embodiments of data and computational functions in support of learning and collaboration. Using these approaches, the dissertation demonstrates: (1) a modified stability constraint for reconstructing gene regulatory network that shows improvement in accuracy of predicted networks, (2) a novel modularity constraint (neighbor norm) for extracting available structures in the data which is validated with Laplacian eigenvalue spectrum, and (3) a hybrid method for estimating overall survival and inferring effective prognosis factors for patients with advanced prostate cancer that improves the accuracy of survival analysis.

Tangible visual analytics: the integration of tangible interactions and computational techniques for biological data visualization and modelling with experts in the loop

Understanding and interpreting the inherently uncertain nature of complex biological systems, as well as the time to an event in these systems, are notable challenges in the field of bioinformatics. Overcoming these challenges could potentially lead to scientific discoveries, for example paving the path for the design of new drugs to target specific diseases such as cancer, or helping to apply more effective treatment for these diseases. In general, reverse engineering of these types of biological systems using online datasets is difficult. In particular, finding a unique solution to these systems is hard due to their complexity and the small sample size of datasets. This remains an unsolved problem due to such uncertainty, and the often intractable solution space of these systems. The term"uncertainty" describes the application-based margin of significance, validity, and efficiency of inferred or predictive models in their ability to extract characteristic properties and features describing the observed state of a given biological system. In this work, uncertainties within two specific bioinformatics domains are considered, namely "gene regulatory network reconstruction" (in which gene interactions/relationships within a biological entity are inferred from gene expression data); and "cancer survivorship prediction" (in which patient survival rates are predicted based on clinical factors and treatment outcomes). One approach to reduce uncertainty is to apply different constraints that have particular relevance to each application domain. In gene network reconstruction for instance, the consideration of constraints such as sparsity, stability and modularity, can inform and reduce uncertainty in the inferred reconstructions. While in cancer survival prediction, there is uncertainty in determining which clinical features (or feature aggregates) can improve associated prediction models. The inherent lack of understanding of how, why and when such constraints should be applied, however, prompts the need for a radically new approach. In this dissertation, a new approach is thus considered to aid human expert users in understanding and exploring inherent uncertainties associated with these two bioinformatics domains. Specifically, a novel set of tools is introduced and developed to assist in evidence gathering, constraint definition, and refinement of models toward the discovery of better solutions. This dissertation employs computational approaches, including convex optimization and feature selection/aggregation, in order to increase the chances of finding a unique solution. These approaches are realized through three novel interactive tools that employ tangible interaction in combination with graphical visualization to enable experts to query and manipulate the data. Tangible interaction provides physical embodiments of data and computational functions in support of learning and collaboration. Using these approaches, the dissertation demonstrates: (1) a modified stability constraint for reconstructing gene regulatory network that shows improvement in accuracy of predicted networks, (2) a novel modularity constraint (neighbor norm) for extracting available structures in the data which is validated with Laplacian eigenvalue spectrum, and (3) a hybrid method for estimating overall survival and inferring effective prognosis factors for patients with advanced prostate cancer that improves the accuracy of survival analysis.

Tangible visual analytics: the integration of tangible interactions and computational techniques for biological data visualization and modelling with experts in the loop

Understanding and interpreting the inherently uncertain nature of complex biological systems, as well as the time to an event in these systems, are notable challenges in the field of bioinformatics. Overcoming these challenges could potentially lead to scientific discoveries, for example paving the path for the design of new drugs to target specific diseases such as cancer, or helping to apply more effective treatment for these diseases. In general, reverse engineering of these types of biological systems using online datasets is difficult. In particular, finding a unique solution to these systems is hard due to their complexity and the small sample size of datasets. This remains an unsolved problem due to such uncertainty, and the often intractable solution space of these systems. The term"uncertainty" describes the application-based margin of significance, validity, and efficiency of inferred or predictive models in their ability to extract characteristic properties and features describing the observed state of a given biological system. In this work, uncertainties within two specific bioinformatics domains are considered, namely "gene regulatory network reconstruction" (in which gene interactions/relationships within a biological entity are inferred from gene expression data); and "cancer survivorship prediction" (in which patient survival rates are predicted based on clinical factors and treatment outcomes). One approach to reduce uncertainty is to apply different constraints that have particular relevance to each application domain. In gene network reconstruction for instance, the consideration of constraints such as sparsity, stability and modularity, can informand reduce uncertainty in the inferred reconstructions. While in cancer survival prediction, there is uncertainty in determining which clinical features (or feature aggregates) can improve associated prediction models. The inherent lack of understanding of how, why and when such constraints should be applied, however, prompts the need for a radically new approach. In this dissertation, a new approach is thus considered to aid human expert users in understanding and exploring inherent uncertainties associated with these two bioinformatics domains. Specifically, a novel set of tools is introduced and developed to assist in evidence gathering, constraint definition, and refinement of models toward the discovery of better solutions. This dissertation employs computational approaches, including convex optimization and feature selection/aggregation, in order to increase the chances of finding a unique solution. These approaches are realized through three novel interactive tools that employ tangible interaction in combination with graphical visualization to enable experts to query and manipulate the data. Tangible interaction provides physical embodiments of data and computational functions in support of learning and collaboration. Using these approaches, the dissertation demonstrates: (1) a modified stability constraint for reconstructing gene regulatory network that shows improvement in accuracy of predicted networks, (2) a novel modularity constraint (neighbor norm) for extracting available structures in the data which is validated with Laplacian eigenvalue spectrum, and (3) a hybrid method for estimating overall survival and inferring effective prognosis factors for patients with advanced prostate cancer that improves the accuracy of survival analysis.

Tangible visual analytics: the integration of tangible interactions and computational techniques for biological data visualization and modelling with experts in the loop

Understanding and interpreting the inherently uncertain nature of complex biological systems, as well as the time to an event in these systems, are notable challenges in the field of bioinformatics. Overcoming these challenges could potentially lead to scientific discoveries, for example paving the path for the design of new drugs to target specific diseases such as cancer, or helping to apply more effective treatment for these diseases. In general, reverse engineering of these types of biological systems using online datasets is difficult. In particular, finding a unique solution to these systems is hard due to their complexity and the small sample size of datasets. This remains an unsolved problem due to such uncertainty, and the often intractable solution space of these systems. The term"uncertainty" describes the application-based margin of significance, validity, and efficiency of inferred or predictive models in their ability to extract characteristic properties and features describing the observed state of a given biological system. In this work, uncertainties within two specific bioinformatics domains are considered, namely "gene regulatory network reconstruction" (in which gene interactions/relationships within a biological entity are inferred from gene expression data); and "cancer survivorship prediction" (in which patient survival rates are predicted based on clinical factors and treatment outcomes). One approach to reduce uncertainty is to apply different constraints that have particular relevance to each application domain. In gene network reconstruction for instance, the consideration of constraints such as sparsity, stability and modularity, can informand reduce uncertainty in the inferred reconstructions. While in cancer survival prediction, there is uncertainty in determining which clinical features (or feature aggregates) can improve associated prediction models. The inherent lack of understanding of how, why and when such constraints should be applied, however, prompts the need for a radically new approach. In this dissertation, a new approach is thus considered to aid human expert users in understanding and exploring inherent uncertainties associated with these two bioinformatics domains. Specifically, a novel set of tools is introduced and developed to assist in evidence gathering, constraint definition, and refinement of models toward the discovery of better solutions. This dissertation employs computational approaches, including convex optimization and feature selection/aggregation, in order to increase the chances of finding a unique solution. These approaches are realized through three novel interactive tools that employ tangible interaction in combination with graphical visualization to enable experts to query and manipulate the data. Tangible interaction provides physical embodiments of data and computational functions in support of learning and collaboration. Using these approaches, the dissertation demonstrates: (1) a modified stability constraint for reconstructing gene regulatory network that shows improvement in accuracy of predicted networks, (2) a novel modularity constraint (neighbor norm) for extracting available structures in the data which is validated with Laplacian eigenvalue spectrum, and (3) a hybrid method for estimating overall survival and inferring effective prognosis factors for patients with advanced prostate cancer that improves the accuracy of survival analysis.

Isaac, The Mountain & The Moon: blended materiality: designing a hybrid picture-book

Innovations in tangible interaction, computer vision and emerging trends in digital reading offer novel opportunities in integrating physical and digital graphic storytelling. Research findings identified design and market opportunities in hybrid publications with blended materiality, that when applied to picture-books, would likely have readers perceiving the hybrid whole as more valuable than the sum of its parts. These findings informed the design of Isaac, The Mountain and The Moon, a hybrid physical-digital picture-book that maintains a singular story-world across media, while allowing affordances specific to each. The book’s design acts as a model that integrates considerate enhancements and affordances of both physical and digital objects; explores strategies that further engage reader communities in digital craft and social editions; encourages learning, curiosity and new media literacy through storytelling and the interactive experience. Presented are the motivations for the book, an elaboration of its design and future evaluation.