Mix&Match: Towards Omitting Modelling Through In-Situ Alteration and Remixing of Model Repository Artifacts in Mixed Reality

The accessibility of tools to model artifacts is one of the core driving factors for the adoption of Personal Fabrication. Subsequently, model repositories like Thingiverse became important tools in (novice) makers' processes. They allow them to shorten or even omit the design process, offloading a majority of the effort to other parties. However, steps like measurement of surrounding constraints (e.g., clearance) which exist only inside the users' environment, can not be similarly outsourced. We propose Mix&Match a mixed-reality-based system which allows users to browse model repositories, preview the models in-situ, and adapt them to their environment in a simple and immediate fashion. Mix&Match aims to provide users with CSG operations which can be based on both virtual and real geometry. We present interaction patterns and scenarios for Mix&Match, arguing for the combination of mixed reality and model repositories. This enables almost modelling-free personal fabrication for both novices and expert makers.Comment: 12 pages, 15 figures, 1 table, To appear in the Proceedings of the ACM Conference on Human Factors in Computing Systems 2020 (CHI'20

Orecchio: Extending Body-Language through Actuated Static and Dynamic Auricular Postures

In this paper, we propose using the auricle – the visible part of the ear – as a means of expressive output to extend body language to convey emotional states. With an initial exploratory study, we provide an initial set of dynamic and static auricular postures. Using these results, we examined the relationship between emotions and auricular postures, noting that dynamic postures involving stretching the top helix in fast (e.g., 2Hz) and slow speeds (1Hz) conveyed intense and mild pleasantness while static postures involving bending the side or top helix towards the center of the ear were associated with intense and mild unpleasantness. Based on the results, we developed a prototype (called Orrechio) with miniature motors, custommade robotic arms and other electronic components. A preliminary user evaluation showed that participants feel more comfortable using expressive auricular postures with people they are familiar with, and that it is a welcome addition to the vocabulary of human body language

Augmenting Pathologists with NaviPath: Design and Evaluation of a Human-AI Collaborative Navigation System

Artificial Intelligence (AI) brings advancements to support pathologists in navigating high-resolution tumor images to search for pathology patterns of interest. However, existing AI-assisted tools have not realized this promised potential due to a lack of insight into pathology and HCI considerations for pathologists' navigation workflows in practice. We first conducted a formative study with six medical professionals in pathology to capture their navigation strategies. By incorporating our observations along with the pathologists' domain knowledge, we designed NaviPath -- a human-AI collaborative navigation system. An evaluation study with 15 medical professionals in pathology indicated that: (i) compared to the manual navigation, participants saw more than twice the number of pathological patterns in unit time with NaviPath, and (ii) participants achieved higher precision and recall against the AI and the manual navigation on average. Further qualitative analysis revealed that navigation was more consistent with NaviPath, which can improve the overall examination quality.Comment: Accepted ACM CHI Conference on Human Factors in Computing Systems (CHI '23

xPath: Human-AI Diagnosis in Pathology with Multi-Criteria Analyses and Explanation by Hierarchically Traceable Evidence

Data-driven AI promises support for pathologists to discover sparse tumor patterns in high-resolution histological images. However, from a pathologist's point of view, existing AI suffers from three limitations: (i) a lack of comprehensiveness where most AI algorithms only rely on a single criterion; (ii) a lack of explainability where AI models tend to work as 'black boxes' with little transparency; and (iii) a lack of integrability where it is unclear how AI can become part of pathologists' existing workflow. Based on a formative study with pathologists, we propose two designs for a human-AI collaborative tool: (i) presenting joint analyses of multiple criteria at the top level while (ii) revealing hierarchically traceable evidence on-demand to explain each criterion. We instantiate such designs in xPath -- a brain tumor grading tool where a pathologist can follow a top-down workflow to oversee AI's findings. We conducted a technical evaluation and work sessions with twelve medical professionals in pathology across three medical centers. We report quantitative and qualitative feedback, discuss recurring themes on how our participants interacted with xPath, and provide initial insights for future physician-AI collaborative tools.Comment: 31 pages, 11 figure