Graphics Interface 2026 · Waterloo, Canada
Development and Evaluation of Sensor-Powered Wireless Smart Wooden Panels
Proceedings of Graphics Interface 2026
Abstract
We explored the technical feasibility of wireless smart wooden panels that integrate conventional sensors and electronics. Prior work has explored either embedding wired, off-the-shelf electronic components in wood or enabling wireless energy transfer, but supporting both simultaneously using existing technology has remained uncertain. Following a validation-through-implementation approach, we developed AccelLumber, a prototype that leverages a commodity RF-based wireless power transfer system to continuously power off-the-shelf sensors and electronic components within wooden furniture panels. We evaluate its performance across panel scalability, power infrastructure requirements, and activity recognition. Our results demonstrate that wireless smart wooden panels are not merely a conceptual idea, but a viable approach to realizing smart environments.
System
Turning a wooden panel into a wireless sensing surface
AccelLumber embeds a receiver array, power-management electronics, Bluetooth communication, and a three-axis accelerometer inside a furniture-grade panel. An RF transmitter hidden beneath the floor supplies continuous power, so the finished surface can sense activity without a wired connection or a battery that needs recharging.
Power characterization
How panel size and furniture height affect coverage
We measured four receiver-array layouts while moving the underfloor transmitter across and beyond each panel footprint. Green cells mark locations that met the prototype’s 5.98 mW operating requirement. The comparison shows why antenna count—not only distance—matters when scaling from stools to desks and tables.
The 3 × 4 panel harvested an average of 25.6 mW at 50 cm and 16.4 mW at 80 cm—both comfortably above the 5.98 mW required by the sensing system.
Furniture demonstrations
Built with familiar woodworking operations
To test whether the panels still behave like a practical building material, we used screws, pilot holes, braces, hinges, and off-the-shelf legs to construct three pieces of furniture. Reserved margins protect the embedded antennas while allowing AccelLumber to join standard wood panels and hardware.
Activity recognition
One model across a desk and a stool
Ten participants performed 13 appliance, tool, cooking, office, and seated activities. The study deliberately trained one model across both furniture types—including actions on an adjacent uninstrumented wood panel—to test whether the sensing approach could generalize beyond a single object or surface location.
High accuracy was maintained while the entire sensing and communication system ran on harvested RF energy, including trials performed on regular wood joined to the powered panel.