We present ANISMA, a software and hardware toolkit to prototype on-skin haptic devices that generate skin deformation stimuli like pressure, stretch, and motion using shape-memory alloys (SMAs). Our toolkit embeds expert knowledge that makes SMA spring actuators more accessible to human–computer interaction (HCI) researchers. Using our software tool, users can design different actuator layouts, program their spatio-temporal actuation and preview the resulting deformation behavior to verify a design at an early stage. Our toolkit allows exporting the actuator layout and 3D printing it directly on skin adhesive. To test different actuation sequences on the skin, a user can connect the SMA actuators to our customized driver board and reprogram them using our visual programming interface. We report a technical analysis, verify the perceptibility of essential ANISMA skin deformation devices with 8 participants, and evaluate ANISMA regarding its usability and supported creativity with 12 HCI researchers in a creative design task.

Data workers are non-professional data scientists who often use scripting languages like R, Python, or MATLAB, and employ an exploratory programming workflow. Current IDEs offer them two main programming modalities: script files and computational notebooks. To understand how these modalities impact work practice, we conducted a study with 21 data workers, and a subsequent larger survey with 62 respondents. Through interviews, walkthroughs, and screen recordings, we collected information about their workflows. Our analysis shows a tension between scripts and computational notebooks. Scripts are more common, better support storage and execution of previous analyses, but hinder experimentation. Notebooks better suit the actual data science workflow, but can become easily unorganized. We discuss how this dual nature of modality usage leads to several issues that affect data workers' workflows, and discuss implications for the design of programming IDEs.

We introduce Springlets, expressive, non-vibrating mechanotactile interfaces on the skin. Embedded with shape memory alloy springs, we implement Springlets as thin and flexible stickers to be worn on various body locations, thanks to their silent operation even on the neck and head. We present a technically simple and rapid technique for fabricating a wide range of Springlet interfaces and computer-generated tactile patterns. We developed Springlets for six tactile primitives: pinching, directional stretching, pressing, pulling, dragging, and expanding. A study placing Springlets on the arm and near the head demonstrates Springlets' effectiveness and wearability in both stationary and mobile situations. We explore new interactive experiences in tactile social communication, physical guidance, health interfaces, navigation, and virtual reality gaming, enabled by Springlets' unique and scalable form factor.

E-Textiles are fabrics that integrate electronic circuits and components. Makers use them to create interactive clothing, furniture, and toys. However, this requires significant manual labor and skills, and using technology-centric design tools. We introduce Sketch&Stitch, an interactive embroidery system to create e-textiles using a traditional crafting approach: Users draw their art and circuit directly on fabric using colored pens. The system takes a picture of the sketch, converts it to embroidery patterns, and sends them to an embroidery machine. Alternating between sketching and stitching, users build and test their design incrementally. Sketch&Stitch features Circuitry Stickers representing circuit boards, components, and custom stitch patterns for wire crossings to insulate, and various textile touch sensors such as pushbuttons, sliders, and 2D touchpads. Circuitry Stickers serve as placeholders during design. Using computer vision, they are recognized and replaced later in the appropriate embroidery phases. We close with technical considerations and application examples.

Ubiquitous computing strives to reach the calm computing state where sensors and actuators disappear from the foreground of our surroundings into the fabric of everyday objects. Despite the great progress in embedded technology, artificial interfaces, such as remote controls and touch screens, remain the dominant media for interacting with smart everyday objects. Motivated by recent advancements in smart textile technologies, we investigate the usability and acceptance of fabric-based controllers in the smart home environment. In this article we describe the development and evaluation of three textile interfaces for controlling a motorized recliner armchair in a living room setting. The core of this contribution is the empirical study with twenty participants that contrasted the user experience of three textile-based interaction techniques to a standard remote control. Despite the slightly lower reliability of the textile interfaces, their overall acceptance was higher. The study shows that the hedonic quality and attractiveness of textile interfaces have higher impact on user acceptance compared to pragmatic qualities, such as efficiency, fluidity of interaction, and reliability. Attractiveness profits from the direct and nearly invisible integration of the interaction device into textile objects such as furniture.

Devices like smartphones, smartwatches, and fitness trackers enable runners to control music, query fitness parameters such as heart rate and speed, or be guided by coaching apps. But while these devices are portable, interacting with them during running is difficult: they usually have small buttons or touchscreens which force the user to slow down to interact with them properly. On-body tapping is an interaction technique that allows users to trigger actions by tapping at different body locations eyes-free. This paper investigates on-body tapping as a potential input technique for runners. We conducted a user study to evaluate where and how accurately runners can tap on their body. We motion-captured participants while tapping locations on their body and running on a treadmill at different speeds. Results show that a uniform layout of five targets per arm and two targets on the abdomen achieved 96% accuracy rate. We present a set of design implications to inform the design of on-body interfaces for runners.