A self-powered wearable structured foam built-in electrode triboelectric sensor system for fall risk detection and vibration hazard monitoring of construction workers

To address the growing need for occupational safety in high-risk environments, we developed a self-powered, intelligent, and adaptable monitoring system based on a structured foam built-in electrode triboelectric nanogenerator (SFBE-TENG). The device integrates a porous, skinless positive layer formed via surface-confined foaming and a barb-like negative layer replicated using a stainless-steel mesh, creating a complementary surface structure. The complementary surface topography and the microcell-induced charge accumulation mechanism jointly contribute to the improved output performance of the SFBE-TENG. A built-in electrode enables multilayer integration, improves environmental durability, and offers mechanical buffering. Deployed at key body positions, SFBE-TENG generates high-fidelity signals in response to fall events. With a gated recurrent unit (GRU) model, the system achieves 94.67 % accuracy in fall detection. When embedded in gloves, it captures hand-transmitted vibration signals during tool use. A convolutional neural network (CNN) extracts frequency features and calculates the equivalent acceleration (a_rms) and daily exposure (A(8)) to classify vibration risk in line with ISO 5349-1: 2016 standards. Integrating sensing, power generation, and mechanical protection, this platform offers a unified solution for real-time fall monitoring and vibration risk assessment, providing a scalable framework for intelligent and proactive safety monitoring systems in industrial settings.