PatchHAR: A MLP-Like Architecture for Efficient Activity Recognition Using Wearables-Reference-Cited by-同舟云学术

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PatchHAR: A MLP-Like Architecture for Efficient Activity Recognition Using Wearables

Published:2024-04 Issue:2 Volume:6 Page:169-181
ISSN:2637-6407
Container-title:IEEE Transactions on Biometrics, Behavior, and Identity Science
language:
Short-container-title:IEEE Trans. Biom. Behav. Identity Sci.

Author:

Wang Shuoyuan¹^ORCID,Zhang Lei¹^ORCID,Wang Xing¹^ORCID,Huang Wenbo²^ORCID,Wu Hao³^ORCID,Song Aiguo⁴^ORCID

Affiliation:

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

2. School of Computer Science and Engineering, Southeast University, Nanjing, China

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

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

Funder

National Nature 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)

Link

http://xplorestaging.ieee.org/ielx7/8423754/10490303/10400955.pdf?arnumber=10400955

Reference57 articles.

1. Dynamic Temperature Scaling in Contrastive Self-Supervised Learning for Sensor-Based Human Activity Recognition

2. A tutorial on human activity recognition using body-worn inertial sensors

3. Imaging and fusing time series for wearable sensor-based human activity recognition

4. Sensor-based and vision-based human activity recognition: A comprehensive survey

5. Deep Triplet Networks with Attention for Sensor-based Human Activity Recognition

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