Abstract
This paper introduces EmbedNet, a method for integrating dense sensor networks into casting objects. With EmbedNet, sensor nodes are seamlessly incorporated into casting objects during fabrication. The process involves extruding base materials like silicone rubber or liquid plastic and a custom-designed sensor strip using a hand-held extruder into a mold tailored to specific applications. The base material mixes with the sensor strip in the mold, and upon curing, the result is an object with a defined shape housing a sensor network. EmbedNet employs a small Host node to access sensor data from all nodes on the strip. Each sensor node is self-contained and provides status indications through an onboard RGB LED. The Host connects with all sensor nodes using just three wires: power, ground, and data. This one-wire communication is facilitated through a custom-designed software serial port for each sensor node. The paper showcases various applications of EmbedNet, including wearables, home sensing, and entertainment devices.
Description
UIST Adjunct ’25, Busan, Republic of Korea
Publisher
ACM|The 38th Annual ACM Symposium on User Interface Software and Technology
Citation
Fangzheng Liu, Artem Dementyev, Irmandy Wicaksono, and Joseph A. Paradiso. 2025. Experiencing EmbedNet: Embedding self-sensing to 3D casting objects. In Adjunct Proceedings of the 38th Annual ACM Symposium on User Interface Software and Technology (UIST Adjunct '25). Association for Computing Machinery, New York, NY, USA, Article 35, 1–4.
Version: Final published version