Data Processing and Sensor Data Fusion Algorithm Application in Smart Safety Helmets

Aiming at the real-time and accuracy requirements of smart safety helmets for personnel status monitoring in complex environments, a multimodal data processing algorithm based on sensor data fusion is studied to improve the detection performance and response speed of safety helmets. Firstly, the dynamic behavior data of personnel are collected using multiple sensors such as the built-in accelerometer, gyroscope and heart rate sensor of the helmet. Then, the Kalman Filtering (KF) algorithm is used to pre-process the sensor data to remove noise and supplement the missing key data. Then, the different sensor data are integrated through the weighted fusion method, and the SVM (Support Vector Machine) and CNN (Convolutional Neural Network) machine learning models are combined to detect and predict abnormal states. Finally, the system transmits the processing results to the central control platform through the wireless communication module to achieve real-time monitoring and early warning. Experiments show that when using Kalman filtering, the application of Kalman filtering in the construction site environment significantly improves the detection accuracy (90%) and reduces the false alarm rate (10%) and missed alarm rate (4%). This method effectively improves the data processing capability of smart safety helmets in dynamic environments and enhances their response speed to dangerous events, providing reliable technical support for the safe application of smart safety equipment in the industrial and construction fields.