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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| Published in: | Pattern recognition letters Vol. 185; pp. 31 - 37 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
01.09.2024
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| Subjects: | |
| ISSN: | 0167-8655 |
| Online Access: | Get full text |
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| Summary: | 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. |
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| ISSN: | 0167-8655 |
| DOI: | 10.1016/j.patrec.2024.07.003 |