This paper introduces WISP, a printable graphene-based wearable system that enables truly imperceptible micro-gesture recognition with sub-10mm finger movements—up to 4 × smaller than existing approaches. While conventional gesture systems require conspicuous hand motions that disrupt social interactions, WISP detects subtle pinch-like micro-gestures that are virtually invisible to observers yet produce distinct force signatures at the wrist through tendon and skin deformation. Our co-designed hardware-software pipeline leverages Laser-Induced Graphene (LIG) technology to create ultra-thin, skin-conformal strain sensor arrays that can be fabricated on-demand using standard laser cutters and achieves 100μVpp noise floor while consuming only 10mW power. Evaluation with 19 participants demonstrates 99.2% hotword detection, 90.2% user-specific recognition, and 82.9% cross-user generalization. WISP maintains high accuracy across real-world conditions including walking and various clothing scenarios, scales to 12-gesture vocabularies, and extends to context-aware object interactions with 93.8% accuracy.

Recent trends in flexible antennas and printed circuit boards present an opportunity to leverage deformable substrates such as textiles to deploy large UHF, VHF and ISM band antenna arrays in smart homes. Low-frequency large antenna arrays are rarely deployed in indoor settings due to their large size which makes them bulky and difficult to deploy. By embedding these arrays on existing surfaces such as curtains, we can improve through-wall sensing, beamforming for IoT devices equipped with low-power radios and indoor localization of Bluetooth tags.

ThumbTrak is a novel wearable input device that recognizes 12 micro-finger poses in real-time. Poses are characterized by the thumb touching each of the 12 phalanges on the hand. It uses a thumb-ring, built with a flexible printed circuit board, which hosts nine proximity sensors. Each sensor measures the distance from the thumb to various parts of the palm or other fingers. ThumbTrak uses a support-vector-machine (SVM) model to classify finger poses based on distance measurements in real-time. A user study with ten participants showed that ThumbTrak could recognize 12 micro finger poses with an average accuracy of 93.6%. We also discuss potential opportunities and challenges in applying ThumbTrak in real-world applications.