Role of Computer Algorithms in Data Acquisition of Smart Safety Helmets

With the widespread application of smart safety helmets in high-risk industries, how to efficiently and accurately collect and analyze data in helmets has become a key challenge to improve safety and comfort. To this end, this paper designs an algorithm based on deep learning and data fusion to optimize the data acquisition and analysis process of smart safety helmets. First, a multi-sensor data acquisition system is designed to monitor the wearer's physiological, motion status and environmental information in real-time. Then, a convolutional neural network (CNN) is used to extract data features, and a long short-term memory (LSTM) network is used to process time series data to accurately identify motion patterns, fall events and fatigue states. Through multimodal data fusion, the robustness and response speed of the system are improved. Experimental results show that the average classification accuracy of the proposed scheme in the motion pattern recognition task is 90.87%. In the fall detection experiment, the overall accuracy of the LSTM algorithm reaches 90.0%. In the fatigue monitoring experiment, the research model has higher accuracy and robustness in the detection of fatigue states. The experimental results verify the application potential of deep learning and multimodal data fusion in smart safety helmets, and provide effective technical support for improving safety and enhancing wearer comfort.