DiamondNet: A Neural-Network-Based Heterogeneous Sensor Attentive Fusion for Human Activity Recognition

With the proliferation of intelligent sensors integrated into mobile devices, fine-grained human activity recognition (HAR) based on lightweight sensors has emerged as a useful tool for personalized applications. Although shallow and deep learning algorithms have been proposed for HAR problems in th...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE transaction on neural networks and learning systems Ročník 35; číslo 11; s. 15321 - 15331
Hlavní autoři:	Zhu, Yida, Luo, Haiyong, Chen, Runze, Zhao, Fang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.11.2024
Témata:	Adaptation models Algorithms Attention Convolutional denoising autoencoders (CDAEs) Convolutional neural networks Correlation Deep Learning Feature extraction global-attention mechanism Graph convolutional networks Human Activities - classification Human activity recognition human activity recognition (HAR) Humans multisensor modality Multisensor systems Neural Networks, Computer Noise reduction Pattern Recognition, Automated - methods self-attention mechanism
ISSN:	2162-237X, 2162-2388, 2162-2388
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!