Force sensing has been widely used for bringing the touch from binary to multiple states, creating new abilities on surface interactions. However, prior proposed force sensing techniques mainly focus on enabling force-applied gestures on certain devices. This paper presents Nail+, a technique using fingernail deformation to enable force touch sensing interactions on everyday rigid surfaces. Our prototype, 3x3 0.2mm strain sensor array mounted on a fingernail, was implemented and conducted with a 12-participant study for evaluating the feasibility of this sensing approach. Result showed that the accuracy for sensing normal and force-applied tapping and swiping can achieve 84.67% on average. We finally proposed two example applications using Nail+ prototype for controlling the interfaces of head-mounted display (HMD) devices and remote screens.

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.

We present FlickBoard, which combines a touchpad and a keyboard into the same interaction area to reduce hand movement between a separate keyboard and touchpad. Our main contribution is automatic mode switching between typing and pointing, and the first system capable of combining a trackpad and a keyboard into an single interaction area without the need for external switches. We developed a prototype by embedding a 58x20 capacitive sensing grid into a soft keyboard cover, and used machine learning to distinguish between moving a cursor (touchpad mode) and entering text (keyboard mode). We conducted experimental studies that show automatic mode switching classification accuracies of 98% are achievable with our technology. Finally, our prototype has a thin profile and can be placed over existing keyboards.

In this paper, we explore using the back of hands for sensing hand gestures, which interferes less than glove-based approaches and provides better recognition than sensing at wrists and forearms. Our prototype, BackHand, uses an array of strain gauge sensors affixed to the back of hands, and applies machine learning techniques to recognize a variety of hand gestures. We conducted a user study with 10 participants to better understand gesture recognition accuracy and the effects of sensing locations. Results showed that sensor reading patterns differ significantly across users, but are consistent for the same user. The leave-one-user-out accuracy is low at an average of 27.4%, but reaches 95.8% average accuracy for 16 popular hand gestures when personalized for each participant. The most promising location spans the 1/8~1/4 area between the metacarpophalangeal joints (MCP, the knuckles between the hand and fingers) and the head of ulna (tip of the wrist).