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Not AvailableGenerating salient and intuitively understood haptic feedback on the human finger through a non-intrusive wearable remains a challenge in haptic device development. Most existing solutions either restrict the hand and finger’s natural range of motion or impede sensory perception, quickly becoming intrusive during dexterous manipulation tasks. Here, we introduce NURing (Non-intrUsive Ring), a tendon-actuated haptic device that provides kinesthetic feedback by deflecting the finger. The NURing is easily donned and doffed, enabling on-demand kinesthetic feedback while leaving the hand and fingers free for dexterous tasks. We demonstrate that the device delivers perceptually salient feedback and evaluate its performance through a series of uniaxial motion guidance tasks. The lightweight NURing device, measuring approximately 220 g, can generate guidance cues at up to 1 Hz, enabling participants to identify target directions in under 3 s with a 1.5° steady-state error, corresponding to a fingertip deviation of less than 11mm. Additionally, it can guide users along complex, smooth trajectories with an average trajectory error of 7°. These findings highlight the effectiveness of fingertip deflection as a kinesthetic feedback modality, enabling precise guidance for real-world applications such as sightless touchscreen navigation, assistive technology, and both industrial and consumer augmented/virtual reality systems.
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This content will become publicly available on April 25, 2026
ARticulate: Interactive Visual Guidance for Demonstrated Rotational Degrees of Freedom in Mobile AR
Mobile Augmented Reality (AR) offers a powerful way to provide spatially-aware guidance for real-world applications. In many cases, these applications involve the configuration of a camera or articulated subject, asking users to navigate several spatial degrees of freedom (DOF) at once. Most guidance for such tasks relies on decomposing available DOF into subspaces that can be more easily mapped to simple 1D or 2D visualizations. Unfortunately, different factorizations of the same motion often map to very different visual feedback, and finding the factorization that best matches a user’s intuition can be difficult. We propose an interactive approach that infers rotational degrees of freedom from short user demonstrations. Users select one or two DOFs at a time by demonstrating a small range of motion, which we use to learn a rotational frame that best aligns with user control of the object. We show that deriving visual feedback from this inferred learned rotational frame leads to improved task completion times on 6DOF guidance tasks compared to standard default reference frames used in most mixed reality applications.
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- Award ID(s):
- 2340448
- PAR ID:
- 10594569
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400713941
- Page Range / eLocation ID:
- 1 to 8
- Subject(s) / Keyword(s):
- Augmented Reality Computational Photography Human-Computer Interaction HCI Mobile Application
- Format(s):
- Medium: X
- Location:
- Yokohama Japan
- Sponsoring Org:
- National Science Foundation
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