Deep learning enabled smart mats as a scalable floor monitoring system

TLDR

Smart building floors can be instrumented with embedded sensors, but existing floor sensors are small‑scale, expensive, power‑hungry, and complex to configure. This work presents a smart floor monitoring system that integrates self‑powered triboelectric mats with deep‑learning analytics. The mats feature unique identity electrode patterns fabricated by a low‑cost, scalable screen‑printing process that allows parallel connections, reducing system complexity and deep‑learning computational cost. The system accurately determines stepping position, activity status, and identity from instantaneous sensor data, establishing a foundation for smart building, healthcare, and security applications.

Abstract

Abstract Toward smart building and smart home, floor as one of our most frequently interactive interfaces can be implemented with embedded sensors to extract abundant sensory information without the video-taken concerns. Yet the previously developed floor sensors are normally of small scale, high implementation cost, large power consumption, and complicated device configuration. Here we show a smart floor monitoring system through the integration of self-powered triboelectric floor mats and deep learning-based data analytics. The floor mats are fabricated with unique “identity” electrode patterns using a low-cost and highly scalable screen printing technique, enabling a parallel connection to reduce the system complexity and the deep-learning computational cost. The stepping position, activity status, and identity information can be determined according to the instant sensory data analytics. This developed smart floor technology can establish the foundation using floor as the functional interface for diverse applications in smart building/home, e.g., intelligent automation, healthcare, and security.