Cargando…
Human Activity Recognition Based on Residual Network and BiLSTM
Due to the wide application of human activity recognition (HAR) in sports and health, a large number of HAR models based on deep learning have been proposed. However, many existing models ignore the effective extraction of spatial and temporal features of human activity data. This paper proposes a d...
Autores principales: | Li, Yong, Wang, Luping |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8778132/ https://www.ncbi.nlm.nih.gov/pubmed/35062604 http://dx.doi.org/10.3390/s22020635 |
Ejemplares similares
-
Automatic Modulation Recognition Based on a DCN-BiLSTM Network
por: Liu, Kai, et al.
Publicado: (2021) -
Aeroengine Working Condition Recognition Based on MsCNN-BiLSTM
por: Zheng, Jinsong, et al.
Publicado: (2022) -
Network Intrusion Detection Method Based on FCWGAN and BiLSTM
por: Ma, Zexuan, et al.
Publicado: (2022) -
A Convolutional Neural Network Face Recognition Method Based on BiLSTM and Attention Mechanism
por: Qi, Xiaobo, et al.
Publicado: (2023) -
Digitization of Handwritten Chess Scoresheets with a BiLSTM Network
por: Majid, Nishatul, et al.
Publicado: (2022)