Sustainable occupancy sensing platform via triboelectric and piezoresistive pressure sensors

Background — Real-time occupancy data enable smart-building systems to optimise HVAC and lighting, yet most floor-sensor solutions require external power or sacrifice detection accuracy. Combining piezoresistive and triboelectric approaches offers a route toward low-maintenance, energy-aware sensing.

Methods — We therefore developed two floor-mat systems: an 8 × 8 (64-pixel) Velostat piezoresistive matrix as a benchmark and a 4 × 4 (16-pixel) triboelectric nanogenerator (TENG) matrix that detects footsteps while harvesting their energy. Both mats interface with an Arduino-based board, Bluetooth link, and custom Python dashboard for real-time monitoring, sampling at 25.6 ms (Velostat) and 20 ms (TENG).

Results — Under realistic use, the Velostat mat achieved 96% entry/exit accuracy, whereas the TENG reached 85% and produced ∼60 μW per footstep.

Conclusion — Although a fully integrated energy-harvesting module is not yet implemented, the modular hardware-software design supports future additions—such as temperature and humidity sensors—and lays the groundwork for scalable, sustainable smart-building management; ongoing work will optimise the harvesting stage and extend the approach to broader environmental-monitoring applications.