Wearable-based behaviour interpolation for semi-supervised human activity recognition-Reference-Cited by-同舟云学术

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Wearable-based behaviour interpolation for semi-supervised human activity recognition

Published:2024-04 Issue: Volume:665 Page:120393
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Duan Haoran^ORCID,Wang Shidong^ORCID,Ojha Varun^ORCID,Wang Shizheng,Huang Yawen,Long Yang,Ranjan Rajiv,Zheng Yefeng

Publisher

Elsevier BV

Reference50 articles.

1. Temporal neighborhood based self-supervised pre-training model for sleep stages classification;Wang,2023

2. Machine learning approaches to understand the influence of urban environments on human's physiological response;Ojha;Inf. Sci.,2019

3. Deep learning in human activity recognition with wearable sensors: a review on advances;Zhang;Sensors,2022

4. Bimodal har-an efficient approach to human activity analysis and recognition using bimodal hybrid classifiers;Venkatachalam;Inf. Sci.,2023

5. Multi-task self-supervised learning for human activity detection;Saeed;Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.,2019

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1. PEFormer: a pixel-level enhanced CNN-transformer hybrid network for face image super-resolution;Signal, Image and Video Processing;2024-07-02

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