Fall detection algorithm based on global and local feature extraction

Falls have become one of the main causes of injury and death among the elderly. A high-accuracy fall detection method can effectively detect falls in the elderly, thereby reducing the probability of injury and mortality. This paper proposes a fall detection algorithm based on global and local featur...

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Veröffentlicht in:Pattern recognition letters Jg. 185; S. 31 - 37
Hauptverfasser: Li, Bin, Li, Jiangjiao, Wang, Peng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.09.2024
Schlagworte:
Convolutional neural network
Dual-stream network
Regional attention module
Transformer
Regional attention module
Transformer
Dual-stream network
Convolutional neural network
ISSN:0167-8655
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Zusammenfassung:Falls have become one of the main causes of injury and death among the elderly. A high-accuracy fall detection method can effectively detect falls in the elderly, thereby reducing the probability of injury and mortality. This paper proposes a fall detection algorithm based on global and local feature extraction. Specifically, we design a dual-stream network, with one branch composed of a convolutional neural network and a regional attention module for extracting local features from images. The other branch consists of an improved Transformer for extracting global features from images. The local and global features are then fused using a feature fusion module for classification, enabling fall detection. Experimental results show that the proposed approach achieves accuracies of 99.55% and 99.75% when tested with UP-Fall Detection Dataset and Le2i Fall Detection Dataset. •A novel fall detection algorithm is proposed to extract local and global features.•A regional attention module is proposed to focus on important features.•An improved Transformer is applied to better extract contextual information.•A feature fusion module is designed to combine local and global features.
ISSN:0167-8655
DOI:10.1016/j.patrec.2024.07.003