Radar Based Fall Detection with Imbalance Data Handling and Data Augmentation-Reference-Cited by-同舟云学术

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Radar Based Fall Detection with Imbalance Data Handling and Data Augmentation

Published:2023-07-18 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Sensors Applications Symposium (SAS)
language:
Short-container-title:

Author:

Sadreazami Hamidreza¹,Khoyani Abhishek²,Amini Marzieh³,Rajan Sreeraman²,Bolic Miodrag⁴

Affiliation:

1. McGill University,Bioengineering Department,Montreal,Canada

2. Carleton University,System and Computer Engineering Department,Ottawa,Canada

3. School of Information Technology, Carleton University,Ottawa,Canada

4. School of Electrical Engineering and Computer Science, University of Ottawa,Ottawa,Canada

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10253663/10253664/10254073.pdf?arnumber=10254073

Reference19 articles.

1. Contactless Fall Detection Using Time-Frequency Analysis and Convolutional Neural Networks

2. Features for micro‐Doppler based activity classification

3. Fall Detection Using Standoff Radar-Based Sensing and Deep Convolutional Neural Network

4. Radar fall motion detection using deep learning

5. CapsFall: Fall Detection Using Ultra-Wideband Radar and Capsule Network

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. State-of-the-art radar technology for remote human fall detection: a systematic review of techniques, trends, and challenges;Multimedia Tools and Applications;2024-07-22

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