Fall Recognition Based on Multi-feature Learning Fusion Cascade Classification

ZHANG Han; OUYANG Junbin; ZHENG Rongjia; CAI Jiequan; GAO Yu

doi:10.6054/j.jscnun.2023042

Journal of South China Normal University (Natural Science Edition) > 2023 > 55(3): 110-118. > DOI: 10.6054/j.jscnun.2023042

ZHANG Han, OUYANG Junbin, ZHENG Rongjia, CAI Jiequan, GAO Yu. Fall Recognition Based on Multi-feature Learning Fusion Cascade Classification[J]. Journal of South China Normal University (Natural Science Edition), 2023, 55(3): 110-118. DOI: 10.6054/j.jscnun.2023042

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Fall Recognition Based on Multi-feature Learning Fusion Cascade Classification

1.
School of Physics and Telecommunications Engineering/Guangdong Provincial Engineering Technology Research Center of Cardiovascular Individual Medicine & Big Data, South China Normal University, Guangzhou 510006, China
2.
School of Electronics and Information Engineering, South China Normal University, Foshan 528234, China

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Received Date: April 18, 2022
Available Online: August 25, 2023

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Abstract

Abstract

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.
- fall recognition,
- cascade classification,
- feature dimension reduction

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