EcoVibWood: Lower Impact Vibration Sensing in Plywood (Video Preview)

EcoVibWood: Lower Impact Vibration Sensing in Plywood Yonghao Shi, Suyuan Liu, Xing-Dong Yang UIST 2026 Video Preview for a UIST 2026 paper In this paper, we present EcoVibWood, an approach that combines alternative sensing materials with an automatic protective mechanism to support lower-impact plywood vibration sensing. We systematically explore commercially available alternatives for the triboelectric and electrode layers, including zinc, iron, gold leaf, graphite, paper, PBAT, and PLA, and evaluate whether they can retain the sensing performance of conventional copper and PTFE configurations. We further introduce a protective mechanism made from wood and bamboo that automatically isolates affected electrodes when screws penetrate the plywood, without requiring electronic components or manual intervention. Our experiments show that several alternative layer combinations achieve signal strength and SNRs comparable to, or exceeding, those of traditional copper--PTFE configurations. We integrate these materials into a working prototype and evaluate its performance across 26 everyday activities. EcoVibWood achieves an average recognition accuracy of 83.3\% using a Random Forest classifier and 94.7\% using a convolutional neural network, demonstrating that lower-impact material substitutions can support robust activity recognition. Web:: https://programs.sigchi.org/uist/2026/program/content/262722