Indutivo, at UIST’18

The Auracle team presented an innovative sensing technology for interacting with wearable devices, during the UIST 2018 in Berlin, Germany.

indutivoAbstract: We present Indutivo, a contact-based inductive sensing technique for contextual interactions. Our technique recognizes conductive objects (metallic primarily) that are commonly found in households and daily environments, as well as their individual movements when placed against the sensor. These movements include sliding, hinging, and rotation. We describe our sensing principle and how we designed the size, shape, and layout of our sensor coils to optimize sensitivity, sensing range, recognition and tracking accuracy. Through several studies, we also demonstrated the performance of our proposed sensing technique in environments with varying levels of noise and interference conditions. We conclude by presenting demo applications on a smartwatch, as well as insights and lessons we learned from our experience.

Read the full paper in ACM digital library:

Jun Gong, Xin Yang, Teddy Seyed, Josh Urban Davis, and Xing-Dong Yang. 2018. Indutivo: Contact-Based, Object-Driven Interactions with Inductive Sensing. In Proceedings of the 31st Annual ACM Symposium on User Interface Software and Technology (UIST ’18). ACM, pp.321-333. DOI: https://doi.org/10.1145/3242587.3242662

Pyro: Thumb-Tip Gesture Recognition Using Pyroelectric Infrared Sensing

The Auracle team presented an innovative new technique for interacting with wearable devices, during the UIST conference last week.

uistf2235-file2Abstract: We present Pyro, a micro thumb-tip gesture recognition technique based on thermal infrared signals radiating from the fingers. Pyro uses a compact, low-power passive sensor, making it suitable for wearable and mobile applications. To demonstrate the feasibility of Pyro, we developed a self-contained prototype consisting of the infrared pyroelectric sensor, a custom sensing circuit, and software for signal processing and machine learning. A ten-participant user study yielded a 93.9% cross-validation accuracy and 84.9% leave-one-session-out accuracy on six thumb-tip gestures. Subsequent lab studies demonstrated Pyro’s robustness to varying light conditions, hand temperatures, and background motion. We conclude by discussing the insights we gained from this work and future research questions.

Read the full paper in ACM digital library:

J. Gong, Y. Zhang, X. Zhou, and X.-D. Yang, “Pyro: Thumb-Tip gesture recognition using pyroelectric infrared sensing,” in Proceedings of the Annual ACM Symposium on User Interface Software and Technology (UIST).    ACM Press, Oct. 2017, pp. 553-563.  Available: http://dx.doi.org/10.1145/3126594.3126615