[HTML][HTML] A novel lightweight deep learning fall detection system based on global-local attention and channel feature augmentation
Y Sha, X Zhai, J Li, W Meng, HHY Tong… - Interdisciplinary Nursing …, 2023 - journals.lww.com
Methods: We used convolutional neural networks and the channel-wise dropout and global-
local attention module to train a lightweight fall detection model on over 10,000 human fall
images from various scenarios. We also applied a channel-based feature augmentation
module to enhance the robustness and stability of the model. Results: The proposed model
achieved a detection precision of 95.1%, a recall of 93.3%, and a mean average precision of
91.8%. It also had a significantly smaller size of 1.09 million model parameters and a lower …
local attention module to train a lightweight fall detection model on over 10,000 human fall
images from various scenarios. We also applied a channel-based feature augmentation
module to enhance the robustness and stability of the model. Results: The proposed model
achieved a detection precision of 95.1%, a recall of 93.3%, and a mean average precision of
91.8%. It also had a significantly smaller size of 1.09 million model parameters and a lower …
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