MESEN: Exploit Multimodal Data to Design Unimodal Human Activity Recognition with Few Labels-Reference-Cited by-同舟云学术

MESEN: Exploit Multimodal Data to Design Unimodal Human Activity Recognition with Few Labels

Published:2023-11-12 Issue: Volume:35 Page:1-14
ISSN:
Container-title:Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems
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Author:

Xu Lilin¹^ORCID,Gu Chaojie¹^ORCID,Tan Rui²^ORCID,He Shibo¹^ORCID,Chen Jiming¹^ORCID

Affiliation:

1. Zhejiang University, Hangzhou, China

2. Nanyang Technological University, Singapore, Singapore

Funder

National Natural Science Foundation of China

Ministry of Education, Singapore

Key Research and Development Program of Zhejiang Province

Fundamental Research Funds for the Central Universities

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3625687.3625782

Reference45 articles.

1. mri: Multi-modal 3d human pose estimation dataset using mmwave, rgb-d, and inertial sensors;An Sizhe;Advances in Neural Information Processing Systems,2022

2. Olasimbo Ayodeji Arigbabu. 2020. Entropy decision fusion for smartphone sensor based human activity recognition. arXiv preprint arXiv:2006.00367 (2020).

3. Chongguang Bi, Guoliang Xing, Tian Hao, Jina Huh, Wei Peng, and Mengyan Ma. 2017. Familylog: A mobile system for monitoring family mealtime activities. In 2017 ieee international conference on pervasive computing and communications (percom). IEEE, 21--30.

4. UTD-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor

5. HMGAN: A Hierarchical Multi-Modal Generative Adversarial Network Model for Wearable Human Activity Recognition;Chen Ling;Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies,2023

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