Parametric Haptics

Versatile Geometry-based Tactile Feedback Devices
Violet Yinuo Han, Abena Boadi-Agyemang, Yuyu Lin, David Lindlbauer, Alexandra Ion

Haptic feedback is important for immersive, assistive, or multi-modal interfaces, but engineering devices that generalize across applications is notoriously difficult. To address the issue of versatility, we propose Parametric Haptics, geometry-based tactile feedback devices that are customizable to render a variety of tactile sensations. To achieve this, we integrate the actuation mechanism with the tactor geometry into passive 3D printable patches, which are then connected to a generic wearable actuation interface consisting of micro gear motors. The key benefit of our approach is that the 3D-printed patches are modular, can consist of varying numbers and shapes of tactors, and that the tactors can be grouped and moved by our actuation geometry over large areas of the skin. The patches are soft, thin, conformable, and easy to customize to different use cases, thus potentially enabling a large design space of diverse tactile sensations.

In our user study, we investigate the mapping between geometry parameters of our haptic patches and users’ tactile perceptions. Results indicate a good agreement between our parameters and the reported sensations, showing initial evidence that our haptic patches can produce a wide range of sensations for diverse use scenarios. We demonstrate the utility of our approach with wearable prototypes in immersive Virtual Reality (VR) scenarios, embedded into wearable objects such as glasses, and as wearable navigation and notification interfaces. We support designing such patches with a design tool in Rhino.

Acknowledgements: This research was supported in part by a research gift from Accenture Technology Labs.

Publication

Violet Yinuo Han, Abena Boadi-Agyemang, Yuyu Lin, David Lindlbauer, Alexandra Ion. 2023. Parametric Haptics: Versatile Geometry-based Tactile Feedback Devices. In Proceedings of UIST ’23. San Francisco, CA. Oct. 29 – Nov. 1, 2023. DOI: https://dl.acm.org/doi/10.1145/3586183.3606766

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