We present FluxLab, a system comprising interactive tools for creating custom 3D-printable shape-changing devices with integrated deformation sensing. To achieve this, we propose a 3D printable nesting structure, consisting of a central SMA channel for sensing and actuation, lattice-based padding in the middle for structural support and controllable elasticity, and parallel helix-based surface wires that preserve the overall form and provide anchoring struts for guided deformation. We developed a design editor to embed these structures into custom 3D models for printing with elastic silicone resin on a consumer-grade SLA 3D printer and minimal post-printing assembly. A deformation authoring tool was also developed for users to build a machine learning-based classifier that distinguishes desired deformation behaviors using inductive sensing. Finally, we demonstrate the potential of our system through example applications, including a self-deformable steamer bowl clip, a remotely controllable gripper, and an interactive desk lamp.

The development of smart textile interfaces is hindered by the inclusion of rigid hardware components and batteries within the fabric, which pose challenges in terms of manufacturability, usability, and environmental concerns related to electronic waste. To mitigate these issues, we propose a smart textile interface and its wireless sensing system to eliminate the need for ICs, batteries, and connectors embedded into textiles. Our technique is established on the integration of multi-resonant circuits in smart textile interfaces, and utilizing near-field electromagnetic coupling between two coils to facilitate wireless power transfer and data acquisition from smart textile interface. A key aspect of our system is the development of a mathematical model that accurately represents the equivalent circuit of the sensing system. Using this model, we developed a novel algorithm to accurately estimate sensor signals based on changes in system impedance. Through simulation-based experiments and a user study, we demonstrate that our technique effectively supports multiple textile sensors of various types.

We present a new approach to address the challenges associated with maintaining the functionality of triboelectric vibration sensors in smart plywood during woodworking operations involving nails and screws. The current state-of-the-art sensor design employs non-overlapping electrodes, which unfortunately leads to significant compromises in terms of signal strength and clarity, particularly in real-world scenarios that involve electromagnetic (EM) interference. To overcome these limitations, we propose a method that enables the woodworker to manually isolate short-circuited electrodes. This method facilitates the creation of sensors using overlapping electrodes, while also incorporating EM shielding, thereby resulting in a substantial improvement in the sensor’s robustness when detecting user activities. To validate the effectiveness of our proposed approach, we conducted a series of experiments, which not only shed light on the drawbacks of non-overlapping electrode designs but also demonstrated the significant improvements achieved through our method.

Tagnoo is a computational plywood augmented with RFID tags, aimed at empowering woodworkers to effortlessly create room-scale smart environments. Unlike existing solutions, Tagnoo does not necessitate technical expertise or disrupt established woodworking routines. This battery-free and cost-effective solution seamlessly integrates computation capabilities into plywood, while preserving its original appearance and functionality. In this paper, we explore various parameters that can influence Tagnoo’s sensing performance and woodworking compatibility through a series of experiments. Additionally, we demonstrate the construction of a small office environment, comprising a desk, chair, shelf, and floor, all crafted by an experienced woodworker using conventional tools such as a table saw and screws while adhering to established construction workflows. Our evaluation confirms that the smart environment can accurately recognize 18 daily objects and user activities, such as a user sitting on the floor or a glass lunchbox placed on the desk, with over 90% accuracy.

Explainable AI (XAI) has established itself as an important component of AI-driven interactive systems. With Augmented Reality (AR) becoming more integrated in daily lives, the role of XAI also becomes essential in AR because end-users will frequently interact with intelligent services. However, it is unclear how to design effective XAI experiences for AR. We propose XAIR, a design framework that addresses when, what, and how to provide explanations of AI output in AR. The framework was based on a multi-disciplinary literature review of XAI and HCI research, a large-scale survey probing 500+ end-users’ preferences for AR-based explanations, and three workshops with 12 experts collecting their insights about XAI design in AR. XAIR’s utility and effectiveness was verified via a study with 10 designers and another study with 12 end-users. XAIR can provide guidelines for designers, inspiring them to identify new design opportunities and achieve effective XAI designs in AR.

NFC (Near-Field Communication) has been widely applied for human-computer interaction (HCI). However, the short sensing distance of NFC requires the users to initiate the tasks with extra effort mostly using their hands, so it is inconvenient to use NFC in hands-busy scenarios. This paper presents an investigation of body-centric interactions between the NFC device users and their surroundings. The exploration is based on the recent development of near-field enabled clothing, which can passively extend an NFC-enabled device’s reading distance to users’ body landmarks. We present an accessible method for fabricating flexible, extensible, and scalable NFC extenders on clothing pieces, and an easy-to-use toolkit for facilitating designers to realize the interactive experiences. The method and toolkit were tested in technical experiments and in a co-creation workshop. The elicited design outcomes and the further exploratory makings generated knowledge for future research and embodied interaction design opportunities.

In this paper, we propose NFCStack, which is a physical building block system that supports stacking and frictionless interaction and is based on near-field communication (NFC). This system consists of a portable station that can support and resolve the order of three types of passive identifiable stackable: bricks, boxes, and adapters. The bricks support stable and sturdy physical construction, whereas the boxes support frictionless tangible interactions. The adapters provide an interface between the aforementioned two types of stackable and convert the top of a stack into a terminal for detecting interactions between NFC-tagged objects. In contrast to existing systems based on NFC or radio-frequency identification technologies, NFCStack is portable, supports simultaneous interactions, and resolves stacking and interaction events responsively, even when objects are not strictly aligned. Evaluation results indicate that the proposed system effectively supports 12 layers of rich-ID stacking with the three types of building block, even if every box is stacked with a 6-mm offset. The results also indicate possible generalized applications of the proposed system, including 2.5-dimensional construction. The interaction styles are described using several educational application examples, and the design implications of this research are explained.

iWood is interactive plywood that can sense vibration based on triboelectric effect. As a material, iWood survives common woodworking operations, such as sawing, screwing, and nailing and can be used to create furniture and artifacts. Things created using iWood inherit its sensing capability and can detect a variety of user input and activities based on their unique vibration patterns. Through a series of experiments and machine simulations, we carefully chose the size of the sensor electrodes, the type of triboelectric materials, and the bonding method of the sensor layers to optimize the sensitivity and fabrication complexity. The sensing performance of iWood was evaluated with 4 gestures and 12 daily activities carried out on a table, nightstand, and cutting board, all created using iWood. Our result suggested over 90% accuracies for activity and gesture recognition.

In this paper, we propose the designs for low cost and 3D-printable add-on components to adapt existing breadboards, circuit components and electronics tools for blind or low vision (BLV) users. Through an initial user study, we identified several barriers to entry for beginners with BLV in electronics and circuit prototyping. These barriers guided the design and development of our add-on components. We focused on developing adaptations that provide additional information about the specific component pins and breadboard holes, modify tools to make them easier to use for users with BLV, and expand non-visual feedback (e.g., audio, tactile) for tasks that require vision. Through a second user study, we demonstrated that our adaptations can effectively overcome the accessibility barriers in breadboard circuit prototyping for users with BLV.

We present Project Tasca, a pocket-based textile sensor that detects user input and recognizes everyday objects that a user carries in the pockets of a pair of pants (e.g., keys, coins, electronic devices, or plastic items). By creating a new fabric-based sensor capable of detecting in-pocket touch and pressure, and recognizing metallic, non-metallic, and tagged objects inside the pocket, we enable a rich variety of subtle, eyes-free, and always-available input, as well as context-driven interactions in wearable scenarios. We developed our prototype by integrating four distinct types of sensing methods, namely: inductive sensing, capacitive sensing, resistive sensing, and NFC in a multi-layer fabric structure into the form factor of a jeans pocket. Through a ten-participant study, we evaluated the performance of our prototype across 11 common objects including hands, 8 force gestures, and 30 NFC tag placements. We yielded a 92.3% personal cross-validation accuracy for object recognition, 96.4% accuracy for gesture recognition, and a 100% accuracy for detecting NFC tags at close distance . We conclude by demonstrating the interactions enabled by our pocket-based sensor in several applications.

We present a novel haptic and audio feedback device that allows blind and visually impaired (BVI) users to understand circuit diagrams. TangibleCircuits allows users to interact with a 3D printed tangible model of a circuit which provides audio tutorial directions while being touched. Our system comprises an automated parsing algorithm which extracts 3D printable models as well as an audio interfaces from a Fritzing diagram. To better understand the requirements of designing technology to assist BVI users in learning hardware computing, we conducted a series of formative inquiries into the accessibility limitations of current circuit tutorial technologies. In addition, we derived insights and design considerations gleaned from conducting a formal comparative user study to understand the effectiveness of TangibleCircuits as a tutorial system. We found that BVI users were better able to understand the geometric, spatial and structural circuit information using TangibleCircuits, as well as enjoyed learning with our tool.

Zippers are common in a wide variety of objects that we use daily. This work investigates how we can take advantage of such common daily activities to support seamless interaction with technology. We look beyond simple zipper-sliding interactions explored previously to determine how to weave foreground and background interactions into a vocabulary of natural usage patterns. We begin by conducting two user studies to understand how people typically interact with zippers. The findings identify several opportunities for zipper input and sensing, which inform the design of Zippro, a self-contained prototype zipper slider, which we evaluate with a standard jacket zipper. We conclude by demonstrating several applications that make use of the identified foreground and background input methods.

We propose a new sensing technique for one-dimensional touch input workable on an interactive thread of less than 0.4 mm thick. Our technique locates up to two touches using impedance sensing with a spacing resolution unachievable by the existing methods. Our approach is also unique in that it locates a touch based on a mathematical model describing the change in thread impedance in relation to the touch locations. This allows the system to be easily calibrated by the user touching a known location(s) on the thread. The system can thus quickly adapt to various environmental settings and users. A system evaluation showed that our system could track the slide motion of a finger with an average error distance of 6.13 mm and 4.16 mm using one and five touches for calibration, respectively. The system could also distinguish between single touch and two concurrent touches with an accuracy of 99% and could track two concurrent touches with an average error distance of 8.55 mm. We demonstrate new interactions enabled by our sensing approach in several unique applications.

We present Fabriccio, a touchless gesture sensing technique developed for interactive fabrics using Doppler motion sensing. Our prototype was developed using a pair of loop antennas (one for transmitting and the other for receiving), made of conductive thread that was sewn onto a fabric substrate. The antenna type, configuration, transmission lines, and operating frequency were carefully chosen to balance the complexity of the fabrication process and the sensitivity of our system for touchless hand gestures, performed at a 10 cm distance. Through a ten-participant study, we evaluated the performance of our proposed sensing technique across 11 touchless gestures as well as 1 touch gesture. The study result yielded a 92.8% cross-validation accuracy and 85.2% leave-one-session-out accuracy. We conclude by presenting several applications to demonstrate the unique interactions enabled by our technique on soft objects.

We present a bimanual text input method on a miniature fingertip keyboard, that invisibly resides on the first segment of a user's index finger on both hands. Text entry can be carried out using the thumb-tip to tap the tip of the index finger. The design of our keyboard layout followed an iterative process, where we first conducted a study to understand the natural expectation of the handedness of the keys in a QWERTY layout for users. Among a choice of 67,108,864 design variations, we identified 1295 candidates offering a good satisfaction for user expectations. Based on these results, we computed an optimized bimanual keyboard layout, while considering the joint optimization problems of word ambiguity and movement time. Our user evaluation revealed that participants achieved an average text entry speed of 23.4 WPM.

We present Capacitivo, a contact-based object recognition technique developed for interactive fabrics, using capacitive sensing. Unlike prior work that has focused on metallic objects, our technique recognizes non-metallic objects such as food, different types of fruits, liquids, and other types of objects that are often found around a home or in a workplace. To demonstrate our technique, we created a prototype composed of a 12 x 12 grid of electrodes, made from conductive fabric attached to a textile substrate. We designed the size and separation between the electrodes to maximize the sensing area and sensitivity. We then used a 10-person study to evaluate the performance of our sensing technique using 20 different objects, which yielded a 94.5% accuracy rate. We conclude this work by presenting several different application scenarios to demonstrate unique interactions that are enabled by our technique on fabrics.

We propose blending the virtual and physical worlds for prototyping circuits using physical proxies. With physical proxies, real-world components (e.g. a motor, or light sensor) can be used with a virtual counterpart for a circuit designed in software. We demonstrate this concept in Proxino, and elucidate the new scenarios it enables for makers, such as remote collaboration with physically distributed electronics components. We compared our hybrid system and its output with designs of real circuits to determine the difference through a system evaluation and observed minimal differences. We then present the results of an informal study with 9 users, where we gathered feedback on the effectiveness of our system in different working conditions (with a desktop, using a mobile, and with a remote collaborator). We conclude by sharing our lessons learned from our system and discuss directions for future research that blend physical and virtual prototyping for electronic circuits.

In this paper, we propose and investigate a new text entry technique using micro thumb-tip gestures. Our technique features a miniature QWERTY keyboard residing invisibly on the first segment of the user's index finger. Text entry can be carried out using the thumb-tip to tap the tip of the index finger. The keyboard layout was optimized for eyes-free input by utilizing a spatial model reflecting the users' natural spatial awareness of key locations on the index finger. We present our approach of designing and optimizing the keyboard layout through a series of user studies and computer simulated text entry tests over 1,146,484 possibilities in the design space. The outcome is a 2×3 grid with the letters highly confining to the alphabetic and spatial arrangement of QWERTY. Our user evaluation showed that participants achieved an average text entry speed of 11.9 WPM and were able to type as fast as 13.3 WPM towards the end of the experiment.

Deaf and hard-of-hearing (DHH) individuals encounter difficulties when engaged in group conversations with hearing individuals, due to factors such as simultaneous utterances from multiple speakers and speakers whom may be potentially out of view. We interviewed and co-designed with eight DHH participants to address the following challenges: 1) associating utterances with speakers, 2) ordering utterances from different speakers, 3) displaying optimal content length, and 4) visualizing utterances from out-of-view speakers. We evaluated multiple designs for each of the four challenges through a user study with twelve DHH participants. Our study results showed that participants significantly preferred speechbubble visualizations over traditional captions. These design preferences guided our development of SpeechBubbles, a real-time speech recognition interface prototype on an augmented reality head-mounted display. From our evaluations, we further demonstrated that DHH participants preferred our prototype over traditional captions for group conversations.

Proper ergonomics improves productivity and reduces risks for injuries such as tendinosis, tension neck syndrome, and back injuries. Despite having ergonomics standards and guidelines for computer usage since the 1980s, injuries due to poor ergonomics remain widespread. We present ActiveErgo, the first active approach to improving ergonomics by combining sensing and actuation of motorized furniture. It provides automatic and personalized ergonomics of computer workspaces in accordance to the recommended ergonomics guidelines. Our prototype system uses a Microsoft Kinect sensor for skeletal sensing and monitoring to determine the ideal furniture positions for each user, then uses a combination of automatic adjustment and real-time feedback to adjust the computer monitor, desk, and chair positions. Results from our 12-person user study demonstrated that ActiveErgo significantly improves ergonomics compared to manual configuration in both speed and accuracy, and helps significantly more users to fully meet ergonomics guidelines.

Drones offer camera angles that are not possible with traditional cameras and are becoming increasingly popular for videography. However, flying a drone and controlling its camera simultaneously requires manipulating 5-6 degrees of freedom (DOF) that needs significant training. We present ARPilot, a direct-manipulation interface that lets users plan an aerial video by physically moving their mobile devices around a miniature 3D model of the scene, shown via Augmented Reality (AR). The mobile devices act as the viewfinder, making them intuitive to explore and frame the shots. We leveraged AR technology to explore three 6DOF video-shooting interfaces on mobile devices: AR keyframe, AR continuous, and AR hybrid, and compared against a traditional touch interface in a user study. The results show that AR hybrid is the most preferred by the participants and expends the least effort among all the techniques, while the users' feedback suggests that AR continuous empowers more creative shots. We discuss several distinct usage patterns and report insights for further design.

Electric current and voltage are fundamental to learning, understanding, and debugging circuits. Although both can be measured using tools such as multimeters and oscilloscopes, electric current is much more difficult to measure because users have to unplug parts of a circuit and then insert the measuring tools in serial. Furthermore, users need to restore the circuits back to its original state after measurements have been taken. In practice, this cumbersome process poses a formidable barrier to knowing how current flows throughout a circuit. We present CurrentViz, a system that can sense and visualize the electric current flowing through a circuit, which helps users quickly understand otherwise invisible circuit behavior. It supports fully automatic, ubiquitous, and real-time collection of amperage information of breadboarded circuits. It also supports visualization of the amperage data on a circuit schematic to provide an intuitive view into the current state of a circuit.

The rise of Maker communities and open-source electronic prototyping platforms have made electronic circuit projects increasingly popular around the world. Although there are software tools that support the debugging and sharing of circuits, they require users to manually create the virtual circuits in software, which can be time-consuming and error-prone. We present CircuitSense, a system that automatically recognizes the wires and electronic components placed on breadboards. It uses a combination of passive sensing and active probing to detect and generate the corresponding circuit representation in software in real-time. CircuitSense bridges the gap between the physical and virtual representations of circuits. It enables users to interactively construct and experiment with physical circuits while gaining the benefits of using software tools. It also dramatically simplifies the sharing of circuit designs with online communities.

Current virtual reality (VR) and augmented reality (AR) devices rely on handheld devices, hand gestures, head tracking, and voice input. This paper presents an initial exploration of using eye expressions as hands-free input modality for head-mounted AR/VR devices (HMDs). We consulted interaction designers and ophthalmologists and enumerated 12 eye expressions, and conducted a 12-person user study to better understand users' ability to perform them as well as preferences.

Augmented Reality (AR) provides real-time information by super-imposing virtual information onto users' view of the real world. Our work is the first to explore how peripheral vision, instead of central vision, can be used to read text on AR and smart glasses. We present PeriText+, a multiword reading interface using rapid serial visual presentation (RSVP). This enables users to observe the real world using central vision, while using peripheral vision to read text. We conducted a lab study to compare reading efficiency among 40 different conditions of text transformation. We also conducted a field study to evaluate the information transfer while using PeriText+ in a real-world walking scenario.

For makers and developers, circuit prototyping is an integral part of building electronic projects. Currently, it is common to build circuits based on breadboard schematics that are available on various maker and DIY websites. Some breadboard schematics are used as is without modification, and some are modified and extended to fit specific needs. In such cases, diagrams and schematics merely serve as blueprints and visual instructions, but users still must physically wire the breadboard connections, which can be time-consuming and error-prone. We present CircuitStack, a system that combines the flexibility of breadboarding with the correctness of printed circuits, for enabling rapid and extensible circuit construction. This hybrid system enables circuit reconfigurability, component reusability, and high efficiency at the early stage of prototyping development.

For live music performances, current Wireless In-Ear Monitoring and Personal Mixing setups require a lot of equipment and wiring. This paper introduces Giggler, a system that makes a Wireless In-Ear Monitoring and Personal Mixing experience, easier to setup, easier to use, faster to control and more accessible for musicians than conventional systems by integrating all equipment into one mobile device per musician. The results of the two user studies that we conducted show that Giggler's User Interface performs up to more than twice as fast as a traditional mixer and indicate that Giggler outperforms a Physical mixer because visual features are added to streamline the channel identification process.