ResNet-SE: Channel Attention-Based Deep Residual Network for Complex Activity Recognition Using Wrist-Worn Wearable Sensors

ResNet-SE: Channel Attention-Based Deep Residual Network for Complex Activity Recognition Using Wrist-Worn Wearable Sensors

Smart mobile devices are being widely used to identify and track human behaviors in simple and complex daily activities. The evolution of wearable sensing technologies pertaining to wellness, living surveillance, and fitness tracking is based on the accurate analysis of people’s behavior...

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Bibliographic Details
Main Authors: Sakorn Mekruksavanich, Anuchit Jitpattanakul, Kanokwan Sitthithakerngkiet, Phichai Youplao, Preecha Yupapin
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Wrist-worn wearable sensor
deep learning
deep residual network
attention mechanism
complex activity recognition
Online Access:https://ieeexplore.ieee.org/document/9771436/

Internet

https://ieeexplore.ieee.org/document/9771436/

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