Robust Adaptive Support Vector Machine Based on Multi-sensor Information Fusion for Human Behavior Recognition

To address the issues of redundant high-dimensional action features, few action behavior classifications, and weak generalization ability of recognition models in traditional human behavior recognition (HBR), this paper proposes an HBR method based on Nonlinear Shannon's Principal Component Analysis (NSPCA) and Multi-Strategy Improved Remora Optimization Algorithm (MSIROA)-Adaptive Bi-kernel Support Vector Machine (ABKSVM). First, the NSPCA is employed to extract features from multiple sources of information and address the issue of nonlinear features within the data. Then, the selected principal component fusion features are input into the MSIROA-ABKSVM model, to achieve recognition of human behaviors. By utilizing an improved SVM, the extracted features are recognized to enhance the ability to identify behavior accurately. The experimental results indicate that the cumulative variance contribution rate of the six principal components selected by the NSPCA method reaches 85 % to simplify the data structure. Using the analysis of performance indicators, the classification method achieved an accuracy of 99.3 % and 99.5 % on the self-collected HBR datasets and the Chinese PLA General Hospital (PLAGH)-HBR dataset, respectively, outperforming other state-of-the-art methods. The results show that the HBR model can identify 33 different human behaviors, providing a new method for improving the recognition rate and effectiveness of daily activity monitoring for the elderly.