The International Association for Automation and Robotics in Construction
Publications / 2025 Proceedings of the 42nd ISARC, Montreal, Canada
Automated Helmet Detection in Construction Sites using UWB and Machine Learning
Ensuring the proper use of personal protective equipment (PPE), particularly helmets, is crucial for enhancing safety on construction sites. This study proposes a novel approach for detecting helmet usage and worker states using ultra-wideband (UWB) localization sensors combined with machine learning algorithms. Unlike traditional sensor-based or image based systems, the proposed method integrates helmet detection into existing proximity warning systems, offering a cost-effective solution without the need for additional hardware. Data was collected from 12 participants in a controlled environment using UWB sensors, and a machine learning model was developed to classify four worker states: standing with a helmet, standing while holding a helmet, walking with a helmet, and walking while holding a helmet. The Gradient Boosting Decision Trees (GBDT) algorithm was selected for model development due to its superior performance. The model achieved an overall accuracy of 74.46% and performed well in detecting unsafe conditions. However, variability was observed in identifying safe states. Additionally, the study explored the impact of worker height on Z-axis localization error, revealing a correlation that suggests the need for height adjusted safety monitoring systems. This research demonstrates that UWB-based systems can enhance PPE monitoring, reduce computational costs, and address privacy concerns, while also highlighting areas for future improvement, such as expanding the model to detect other PPE and refining its ability to differentiate between worker postures.
