Haptic feedback is important for immersive, assistive, or multimodal 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.
https://doi.org/10.1145/3586183.3606766
In this paper, we introduce \textit{MagKnitic}, a novel approach to integrate passive force feedback and binary sensing into fabrics via digital machine knitting. Our approach utilizes digital fabrication technology to enable haptic interfaces that are soft, flexible, lightweight, and conform to the user's body shape. Despite these characteristics, our interfaces provide diverse, interactive, and responsive force feedback, expanding the design space for haptic experiences. \textit{MagKnitic} provides scalable and customizable passive haptic sensations by utilizing the attractive force between ferromagnetic yarns and permanent magnets, both of which are seamlessly integrated into knitted fabrics. Moreover, we present a binary sensing capability based on the resistance drop resulting from the activated electrical path between the integrated magnets and ferromagnetic yarn upon direct contact. We offer parametric design templates for users to customize \textit{MagKnitic} layouts and patterns. With various design layouts and combinations, \textit{MagKnitic} supports passive haptics interactions of linear, polar, angular, planar, radial, and user-defined motions. We perform a technical evaluation of the passive force feedback and the binary sensing capabilities with different machine knitting layouts and patterns, embedded magnet sizes, and interaction distances. In addition, we conduct two user studies to validate the effectiveness of \textit{MagKnitic}. Finally, we demonstrate various application scenarios, including wearable input interfaces, game controllers, passive VR/AR wearables, and interactive furniture coverings.
https://doi.org/10.1145/3586183.3606765
The tactile sensation of textiles is critical in determining the comfort of clothing. For remote use, such as online shopping, users cannot physically touch the textile of clothes, making it difficult to evaluate its tactile sensation. Tactile sensing and actuation devices are required to transmit the tactile sensation of textiles. The sensing device needs to recognize different garments, even with hand-held sensors. In addition, the existing actuation device can only present a limited number of known patterns and cannot transmit unknown tactile sensations of textiles. To address these issues, we propose Telextiles, an interface that can remotely transmit tactile sensations of textiles by creating a latent space that reflects the proximity of textiles through contrastive self-supervised learning. We confirm that textiles with similar tactile features are located close to each other in the latent space through a two-dimensional plot. We then compress the latent features for known textile samples into the 1D distance and apply the 16 textile samples to the rollers in the order of the distance. The roller is rotated to select the textile with the closest feature if an unknown textile is detected.
https://doi.org/10.1145/3586183.3606764
Wearable robotic limbs (WRLs) augment human capabilities through robotic structures that attach to the user’s body. While WRLs are intensely researched and various device designs have been presented, it remains difficult for non-roboticists to engage with this exciting field. We aim to empower interaction designers and application domain experts to explore novel designs and applications by rapidly prototyping personalized WRLs that are customized for different tasks, different body locations, or different users. In this paper, we present WRLKit, an interactive computational design approach that enables designers to rapidly prototype a personalized WRL without requiring extensive robotics and ergonomics expertise. The body-aware optimization approach starts by capturing the user’s body dimensions and dynamic body poses. Then, an optimized fabricable structure of the WRL is generated for a desired mounting location and workspace of the WRL, to fit the user’s body and intended task. The results of a user study and several implemented prototypes demonstrate the practical feasibility and versatility of WRLKit.
https://doi.org/10.1145/3586183.3606748
Electrical muscle stimulation (EMS) became a popular method for force-feedback without mechanical-actuators. While much has been written about the advantages of EMS, not much work has investigated circumventing its key limitations: (1) as impulses traverse the skin, they cause an uncomfortable “tingling”; (2) impulses are delivered via gelled-electrodes, which not only require direct skin contact (must be worn under clothes); but, also (3) dry up after a few hours. To tackle these, we explore switching from electrical to magnetic muscle stimulation (MMS), via electromagnetic fields generated by coils. The first advantage is that MMS coils do not require direct skin contact and can actuate up to 5 cm away (Study#1)—this enables applications not possible with EMS, such as stimulation over the clothes and without ever replacing electrodes. Second, and more important, MMS results in ~50 % less discomfort caused by tingling than EMS (Study#2). We found that reducing this tingling discomfort has two downstream effects for interactive systems: (1) participants rated MMS force-feedback as more realistic than that of EMS (Study#3); and (2) participants could more accurately perceive the pose actuated by the interactive system (Study#4). Finally, we demonstrated applications where our proposed switch from EMS to MMS improves user experience, including for VR feedback, gaming, and pose-control.
https://doi.org/10.1145/3586183.3606812
SleeveIO is a modular and reconfigurable hardware platform for rapid prototyping of multimodal wearable haptic feedback interactions. SleeveIO features engineered machine-knitted sleeve and band substrates, and five categories of haptic feedback actuator modules including vibrotactors, bellows, muscles, suction/puffing cups, and quad-chamber actuators. A universal magnetic attachment mechanism unifies the different types of actuators, enabling countless multimodal haptic experiences involving combinations of different actuator types in different configurations. SleeveIO is compatible with a variety of hardware/software control platforms, such as FlowIO [42], which enables individual control of each haptic actuator and makes the system battery-powered and untethered. This paper presents the SleeveIO platform in detail along with replication resources, a novel generalized approach to making different types of haptic actuators modular and interoperable, new application possibilities enabled by SleeveIO, and a pilot assessment of the viability of the platform as a whole and each module individually.
https://doi.org/10.1145/3586183.3606739