Understanding and Improving Channel Attention for Human Activity Recognition by Temporal-Aware and Modality-Aware Embedding-Reference-Cited by-同舟云学术

Understanding and Improving Channel Attention for Human Activity Recognition by Temporal-Aware and Modality-Aware Embedding

Author:

Han Chaolei¹^ORCID,Zhang Lei¹^ORCID,Tang Yin¹^ORCID,Xu Shige¹^ORCID,Min Fuhong¹^ORCID,Wu Hao²^ORCID,Song Aiguo³^ORCID

Affiliation:

1. School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, China

2. School of Information Science and Engineering, Yunnan University, Kunming, China

3. School of Instrument Science and Engineering, Southeast University, Nanjing, China

Funder

National Science Foundation of China

Industry-Academia Cooperation Innovation Fund Projection of Jiangsu Province

Natural Science Foundation of Jiangsu Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

Reference48 articles.

1. Deep, convolutional, and recurrent models for human activity recognition using wearables;hammerla;Proc 24th Int Joint Conf Artif Intell,2016

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3. Modaldrop: Modality-Aware Regularization for Temporal-Spectral Fusion in Human Activity Recognition;ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2023-06-04