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• We developed three working prototypes of smart rings embedded with multiple (4, 6, and 8) thermoelectric coolers (TECs), of various sizes, which can be used with the in-ring spatial thermal patterns described in the paper. • Our pilot study investigated how well users could localized the in-ring thermal feedback with three different settings. Results showed that users could reliably recognize 4 points with cold stimulation (97.2% accuracy). In the two main experiments, we investigated the use of 4 in-ring TECs to achieve two combinations of spatial thermal patterns (i.e. the neighboring or opposite patterns). The results revealed three neighboring patterns and five opposite patterns that were reliably recognized by the participants with the accuracy above 80%. • The follow-up studies showed that it could be confusing for users by combining four single-spot cold stimulations, three neighboring patterns, and five opposite patterns in the same group (average accuracy: 50.2%). On the other hand, the participants could identify the thermal patterns in the combined group of the single-spot cold stimulations and the neighboring patterns (average accuracy: 85.3%), and the combined group of the single-spot cold stimulations and the opposite patterns (average accuracy: 89.3%). This suggested it could feasible to use these combined group of thermal patterns as thermal icons for information representation. • We further conducted three design workshops, involving six product/interface designers. The designers suggested different mappings between the given thermal patterns and the information, including direction cueing through single-spot and neighboring patterns, artifact comparison through opposite patterns, notifying incoming calls/messages from different persons with different locations and temperatures of the TECs, etc. In this paper, we investigate the use of thermal feedback on a smart ring with multiple thermoelectric coolers (TECs). Our prototype aims to offer an increased expressivity with spatial thermal patterns. Our pilot study showed that users could reliably recognize 4 single points with cold stimulation (97.2% accuracy). In the following two main experiments, we investigated the use of 4 in-ring TECs to achieve two categories of spatial thermal patterns by combining two neighboring or opposite elements. The results revealed three neighboring patterns and five opposite patterns that could be reliably recognized by the participants with the average accuracy above 80%. A follow-up experiment suggested that it could be confusing for users by combining four single-spot cold stimulations, three neighboring patterns, and five opposite patterns in the same group (average accuracy: 50.2%). We conducted two more follow-up studies, showing that the participants could identify the thermal patterns in the combined group of the single-spot cold stimulations and the neighboring patterns (average accuracy: 85.3%), and the combined group of the single-spot cold stimulations and the opposite patterns (average accuracy: 89.3%). We further conducted three design workshops, involving six product/interface designers, to investigate the potential applications of these thermal patterns. The designers suggested different mappings between the given thermal patterns and the information, including direction cueing through single-spot and neighboring patterns, artifact comparison through opposite patterns, notifying incoming calls/messages from different persons with different locations and temperatures of the TECs, etc. This demonstrated interest in spatial thermal patterns in smart rings not only for notifications but also for various everyday activities. [ABSTRACT FROM AUTHOR]

In this research, we conducted an elicitation study with 54 participants to evaluate user-defined mobile gestures given three types of operations, handheld postures, and interaction spaces, respectively, to investigate the effects of these factors on mental model. The results of the elicitation study revealed that each of the three factors significantly affected the users' mental models, which could be reflected by the characteristics (e.g., nature, number of steps, and spatial utility) of the user-defined mobile gestures. In addition, we found that users tended to assign different roles to mid-air and on-screen gestures based on their motor ability given a handheld gesture and their interpretation of the characteristics and requirements of an operation. We further derived a set of gestures commonly defined in the elicitation study for a list of representative operations, and we share our insights into users' mental models and the implications on future mobile-gesture design in different contexts. [ABSTRACT FROM AUTHOR]

Paper, as a traditional material for art and communication, shows great potential as a medium for organic user interfaces, with its ubiquity and flexibility. However, controlling and powering the sensors and actuators that enable interactive paper-crafts has not been fully explored. We present a method of selective inductive power transmission (SIPT) to support interactive paper-crafts. The novelty of this method is that the power transmitter can be controlled to selectively activate one specific receiver at a time through inductive power transferring with multiple receivers. This was achieved by changing the output frequency of the power transmitter to match the impedance of the receivers. The receivers could be embedded or printed to drive paper-crafts. Based on inductor–capacitor oscillating circuit and a function generator with a power amplifier, we developed two different prototypes of SIPT. By comparing the performance of both prototypes, we discussed the advantages and disadvantages of the two systems, and their applications in different contexts of paper-crafts. In addition, we proposed the instructions for using SIPT in developing interactive paper-crafts. With this technology and instructions, we hope to facilitate users to easily design new types of paper-craft systems without being concerned about the arrangement of wire connections to power supply on a massive scale. [ABSTRACT FROM AUTHOR]

The electrovibration tactile display could render the tactile feeling of different textured surfaces by generating the frictional force through voltage modulation. When a user is sliding his/her finger on the display surface, he/she can feel the frictional texture. However, it is not trivial to prepare and fine-tune the appropriate frictional signals for haptic design and texture simulation. In this paper, we present a deep-learning-based framework to generate the frictional signals from the textured images of fabric materials. The generated frictional signal can be used for the tactile rendering on the electrovibration tactile display. Leveraging GANs (Generative Adversarial Networks), our system could generate the displacement-based data of frictional coefficients for the tactile display to simulate the tactile feedback of different fabric materials. Our experimental results show that the proposed generative model could generate the frictional-coefficient signals visually and statistically close to the ground-truth signals. The following user studies on fabric-texture simulation show that users could not discriminate the generated and the ground-truth frictional signals being rendered on the electrovibration tactile display, suggesting the effectiveness of our deep-frictional-signal-generation model. [Display omitted] • The deep-learning-based image-to-friction generation framework for tactile simulation of fabric material. • The augmented visual-to-frictional database based on HapTex for image-to-friction generation. • The technical experiment of frictional-coefficient signal generation evidencing the performance of the proposed generative model. • The user-perception experiment validating the effectiveness of generated signals for tactile simulation of fabrics on the electrovibration tactile display. [ABSTRACT FROM AUTHOR]

• We construct a lexical circuit-event database for modeling novice users' natural-language behaviors on physical-computing system design and development. • For proof of concept, we develop FrtizBot, a BiLSTM-CRF-based system leveraging natural-language interaction for designing and creating Arduino based physical-computing systems. • We conduct a user study showing the effectiveness of FritzBot on sup1 porting novice users' physical-computing task through natural-language interaction. Creating physical-computing systems, especially selecting correct electronic components, assembling the circuit, and implementing the program, can be challenging for novice users. In this paper, we present FritzBot, a data-driven conversational agent supporting novice users on creating physical-computing systems through natural-language interaction. FritzBot is built upon the structure of a BiLSTM-CRF (bi-directional Long Short-term Memory Network and Conditional Random Field) neural network, as a plug-in for Fritzing. The neural network is trained on a lexical circuit-event database derived from 152 students' reports on their physical-computing course projects. By processing the user's textual description on his/her physical-computing idea, FritzBot can extract the causal relationships between the input and the output events, identify the corresponding electronic components, and generate the Arduino-based circuit and the code along with the step-by-step construction guidelines. Our user study shows that compared to the original Arduino software and the circuit-autocompletion software available in the commercial market, FritzBot significantly shortens the time spent, reduces the perceived workload, and enhances the satisfaction/joy for inexperienced users on designing and prototyping physical-computing systems. [ABSTRACT FROM AUTHOR]