Faster R-BERT multimodal fusion real-time psychological stress recognition system

The psychological stress problems among college students are increasingly prominent, requiring efficient and objective identification methods.However, existing real-time systems struggle to balance accuracy with processing speed and lack deep integration of multi-source information (such as expressions, voices, and texts).This study proposes a real-time recognition system based on faster robust bidirectional encoder representations from transformers multimodal fusion, significantly improving computing efficiency through an innovative lightweight fusion mechanism.Experiments on public datasets show the system achieves 86.5% accuracy in stress recognition, significantly improving on traditional methods (e.g., 73.2% for single-modal convolutional neural network).Its inference speed meets real-time requirements (30 fps), with the key area under the curve indicator increasing to 0.91 (from 0.82).This study provides an effective approach for non-intrusive, real-time psychological state monitoring in campus environments.