Fall Recognition Based on Multi-feature Learning Fusion Cascade Classification

To solve the problem that the training errors of different fall methods cannot converge sufficiently, which leads to poor classification effect, a Cascade Fall Recognition (CFR) scheme for cascade classification is proposed based on the extraction of three-dimensional features of acceleration, angular velocity and combined acceleration of wearable devices. At the same time, to reduce the complexity of Cascade serial operation, a low-complexity Cascade Fall Recognition (LCFR) scheme based on dimensionality reduction of empirical features is further proposed. Finally, for SisFall dataset, a single multi-classifier scheme, CFR scheme and LCFR scheme were used to perform four classifications (daily behavior, forward fall, lateral fall and backward fall), and the F1-Score and training time complexity were compared. The experimental results show that the F1-Score of CFR scheme is 98.56% in the four-category fall task classification. On the premise of approaching lossless F1-score, the training time complexity of LCFR scheme is 61.11% lower than that of CFR scheme, and the F1-score of scheme is higher than that of similar fall recognition schemes.