Attention-Based Residual BiLSTM Networks for Human Activity Recognition

Attention-Based Residual BiLSTM Networks for Human Activity Recognition

Human activity recognition (HAR) commonly employs wearable sensors to identify and analyze the time series data collected by them, enabling the recognition of specific actions. However, the current fusion of convolutional and recurrent neural networks in existing approaches encounters difficulties w...

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Bibliographic Details
Main Authors: Junjie Zhang, Yuanhao Liu, Hua Yuan
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Human activity recognition
wearable sensors
attention mechanism
residual block
Online Access:https://ieeexplore.ieee.org/document/10234401/

Internet

https://ieeexplore.ieee.org/document/10234401/

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