Search arXivSearch

arXiv · 2608.11871

ATOM: Geometry-Aware Microgesture towards Object-Agnostic Tangible Interaction

Abstract

This paper presents ATOM, an integrated framework towards agnostic and tangible object interactions with microgestures. Our goal is to support microgesture interactions across different everyday objects, with the capability to automatically leverage the geometric affordance of each object. We formulate a fingertip-aware detection pipeline to leverage generative 2D and 3D models for geometry enhancement and refinement. We then introduce a usability-based method to prioritize the detected elements based on their ergonomic suitability for interactions. Building on this foundation, we further develop an AR system to transform everyday handheld objects into tangible user interfaces with 0D, 1D, and 2D microgesture interactions. Across transitions among everyday cooking objects of varying shapes and sizes, ATOM outperformed ablation baselines in task completion, usability (SUS), and workload (NASA-TLX). A further study with 10 objects demonstrates ATOM's generalizability across objects and grasps, highlighting its potential towards fluid, object-agnostic tangible interaction in real-world AR scenarios.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yinqiao Wang, Hao Xu, Qixuan Liu, Shengdong Zhao, Pheng-Ann Heng, Chi-Wing Fu. 2026-08-12. ATOM: Geometry-Aware Microgesture towards Object-Agnostic Tangible Interaction. https://doi.org/10.1145/3830398.3830664

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

TrialCompass: Visual Analytics for Enhancing the Eligibility Criteria Design of Clinical Trials

Eligibility criteria play a critical role in clinical trials by determining the target patient population, which significantly influences the outcomes of medical interventions. However, current approaches for designing eligibility criteria have limitations to support interactive exploration of the large space of eligibility criteria. They also ignore incorporating detailed characteristics from the original electronic health record (EHR) data for criteria refinement. To address these limitations, we proposed TrialCompass, a visual analytics system integrating a novel workflow, which can empower clinicians to iteratively explore the vast space of eligibility criteria through knowledge-driven and outcome-driven approaches. TrialCompass supports history-tracking to help clinicians trace the evolution of their adjustments and decisions when exploring various forms of data (i.e., eligibility criteria, outcome metrics, and detailed characteristics of original EHR data) through these two approaches. This feature can help clinicians comprehend the impact of eligibility criteria on outcome metrics and patient characteristics, which facilitates systematic refinement of eligibility criteria. Using a real-world dataset, we demonstrated the effectiveness of TrialCompass in providing insights into designing eligibility criteria for septic shock and sepsis-associated acute kidney injury. We also discussed the research prospects of applying visual analytics to clinical trials.

cs.HC

PRIMMDebug: Teaching Secondary School Students a Reflective Approach to Debugging

Debugging is a challenging and infuriating experience for many secondary school students learning their first text-based programming language. One frequent problem is the lack of reflection in students' debugging strategies, which makes error resolution unlikely and teacher reliance common. Tools that encourage more reflective and teacher-independent debugging may foster more success with fixing errors, but are lacking. This paper presents PRIMMDebug, an approach for teaching the debugging process to secondary school students. PRIMMDebug consists of an online tool that takes students through the steps of a pedagogical process based on PRIMM, a framework for teaching programming. The tool encourages written articulation throughout the debugging process and limits students' ability to run and edit code at certain stages. A classroom study with PRIMMDebug found a general reluctance among students to engage with the reflection it promotes, despite teachers appreciating this emphasis on reflection. We end by suggesting three considerations for future pedagogical debugging research and tooling: balance structure and flexibility, teach shorter debugging heuristics, and use tooling early on in students' programming journey.

cs.HC

Mirror Skin: In Situ Visualization of Robot Touch Intent on Robotic Skin

Effective communication of robot touch intent is essential for safe and predictable physical human-robot interaction. While intent communication has been widely studied, existing approaches lack the spatial specificity and semantic depth necessary to efficiently convey robot touch intent. We present Mirror Skin, a cephalopod inspired concept that mirrors in-situ visual representations of a human's body parts onto the corresponding robot's touch region to communicate who shall initiate touch, where it will occur, and when it is imminent. We informed the design of Mirror Skin through a structured design exploration with experts and demonstrate the real-world feasibility of Mirror Skin with a proof-of-concept prototype. User studies in VR and with the physical prototype showed that Mirror Skin significantly improves accuracy and response times for interpreting touch intent and improves the user experience during physical human-robot interactions.

cs.HC