Coding the gestures people use when interacting with tangible devices can aid interface design by making interfaces more intuitive and consistent. Past work devotes very little space to developing coding schemes for user-defined gesture sets. A generalizable coding scheme for gestures made by the human hand is developed for tangible devices using inspiration from predetermined time systems (PTS) from human factors engineering. The coding scheme is generic, adaptable to any hand motion proposal and has features such as duration invariance. Case study examples demonstrate its ability to provide consistent labels for hand interactions with tangible devices. Coding scheme labels are also balanced, having neither too many nor too few distinct gestures. This paper can help teach and create coding schemes for gesture elicitation studies performed by human-computer interaction researchers. The larger societal impact of this work is to help advance scientific methods for beneficial interface design within HCI/HRI by combination with human factors theory.

Machine learning becomes more prevalent in specialized domains such as medicine and biology every year, but domain expert trust in machine learning continues to lag behind. Researchers have explored increasing rational trust in AI but little research exists focusing on systems that foster affective and normative trust between domain experts and data scientists who create the models. Tools like Project Jupyter have attempted to bridge this gap between data scientists and domain experts, but failed to see uptake in applied fields or to promote collaboration through co-located synchronous work. To address this we present a proof-of-concept tabletop interactive machine learning system for synchronous, co-located model fine tuning. We tested our system with biology experts and data scientists on a cell biology dataset. Results show that our system promotes interactions between domain experts, data scientists, and the model-in-training and fosters domain expert affective and normative trust in the resulting AI model.

In biology, microscopy data from thousands of individual cellular events presents challenges for analysis and problem solving. These include a lack of visual analysis tools to complement algorithmic approaches for tracking important but rare cellular events, and a lack of support for collaborative exploration and interpretation. In response to these challenges, we have designed and implemented Tangible Chromatin, a tangible and multi-surface system that promotes novel analysis of complex data generated from high-content microscopy experiments. The system facilitates three specific approaches to analysis: it (1) visualizes the detailed information and results from the image processing algorithms, (2) provides interactive approaches for browsing, selecting, and comparing individual data elements, and (3) expands options for productive collaboration through both independent and joint work. We present three main contributions: (i) design requirements that derive from the analytical goals of DNA replication biology, (ii) tangible and multi-surface interaction techniques to support the exploration and analysis of datasets from high-content microscopy experiments, and (iii) the results of a user study that investigated how the system supports individual and collaborative data analysis and interpretation tasks.

Advances in biology and computational power have led to the availability of large biological data sets, yet these advances raise new design challenges. Designers must build effective tools that cater to the needs of biologists and data scientists in order to visually explore and manipulate data for modeling and analysis. We present the Tangible Tensors system, a new tensor-based visualization and tangible manipulation tool that serves to improve functionality over previous data analytics approaches. We designed a platform that supports iterative exploration of the solution space and better interpretation tools for biologists. User study results indicate that our system is easy to learn and use, and useful for data modeling and analysis tasks.

Biological networks analysis has become a systematic and large-scale phenomenon. Most biological systems are often difficult to interpret due to the complexity of relationships and structural features. Moreover, existing primarily web-based interfaces for biological networks analysis often have limitations in usability as well as in supporting high-level reasoning and collaboration. Interactive surfaces coupled with tangible interactions offer opportunities to improve the comparison and analysis of large biological networks, which can aid researchers in making hypotheses and forming insights. We present Tangible BioNets, an active tangible and multi-surface system that allows users with diverse expertise to explore and understand the structural and functional aspects of biological organisms individually or collaboratively. The system was designed through an iterative co-design process and facilitates the exploration of biological network topology, catalyzing the generation of new insights. We describe a first informal evaluation with expert users and discuss considerations for designing tangible and multi-surface systems for large biological datasets.

We developed a custom tangible that uses LED arrays around the edges as a low-resolution display to provide real-time visual feedback on the current state of the system. We developed a guideline for mapping different types of edge feedback to different tangible interactions. We evaluated its effectiveness in an informal user study where users interacted with a tabletop and tangible system with the edge feedback enabled. Results suggest that edge feedback provides a better understanding of the system.

Rapid growth in the volume of cancer DNA sequencing data has not been matched by an increase in our ability to understand, explore, and interpret this data. There is a need for new tools that can enhance analyses in order to enable us to interpret the data, discover new or unexpected information, and ultimately form insights. We present Tangible mtDNA, an active tangible and tabletop system that allows multiple users with diverse expertise to collaborate in exploring and understanding mitochondrial DNA sequencing data in breast cancer patients. Five expert biologists evaluated the system and found it to be effective for data exploration and useful in supporting understanding, collaboration and discussion of DNA datasets.

Actibles are an open source hardware/software platform for creating active tangibles. Actibles contain a smartwatch core, which eases both hardware and software development, and enables application developers to leverage various web technologies. The smartwatch core is augmented by custom hardware that enables an expanded set of tangible interactions, including shaking, tilting, stacking and neighbouring, as well as on-screen gestures and integrated LED feedback. Actibles can be used both independently or in conjunction with other devices, such as interactive tabletops. We describe the Actible's technical specifications and demonstrate several example applications.

Biological data is becoming so complex, it is difficult for scientists and other professionals to interpret and understand it. New tools are needed to better support the manipulation and understanding of data in order to improve analyses and the formation of new hypotheses. Tangible mtDNA is an active tangible and tabletop system that allows multiple users with diverse expertise to collaborate in exploring and understanding mitochondrial DNA sequencing data in breast cancer patients. In an evaluation of the system, 5 expert biologists found it to be effective for data exploration and useful in supporting understanding, collaboration and discussion of DNA datasets.

We present Active Pathways, an active tangible and tabletop system that aims to support collaborative discovery and learning in biochemical modeling. Our work extends ideas from embodied cognition that suggest that interactive systems that support model building by coupling actions made with the motor system to the dynamic properties of external and internal models can enhance the potential for insight and discoveries. We describe the motivation for our work and the design and development of our system. We also present the results of a user study with pairs of novice modelers, which suggest that our system is not only easy to use and learn but also successful at facilitating an understanding of complex systems and supporting collaboration.

We present Sparse Tangibles, a tabletop and active tangible-based framework to support cross-platform, collaborative gene network exploration using a Web interface. It uses smartwatches as active tangibles to allow query construction on- and off-the-table. We expand their interaction vocabulary using inertial sensors and a custom case. We also introduce a new metric for measuring the "confidence level" of protein and genetic interactions. Three expert biologists evaluated the system and found it fun, useful, easy to use, and ideal for collaborative explorations.

Clustering of genes using their expression data has been a major topic in recent years. A large amount of gene expression data even in time series are obtained by microarray technology. Finding gene clusters with similar functions and interconnecting genes by networks has an important role in mining biological gene functional analysis. In this paper, Two Phase Functional Clustering has been presented as a new approach in gene clustering. The proposed approach is based on finding functional patterns of time series gene expression data by Fuzzy C-Means (FCM) and K-means methods. The gene function similarities over a number of experimental conditions are extracted using Pearson correlation between expression patterns of genes. This leads to visualize genes interconnections.