A Survey on Recent Advances in Fall Detection Systems Using Machine Learning Formalisms-Reference-Cited by-同舟云学术

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A Survey on Recent Advances in Fall Detection Systems Using Machine Learning Formalisms

Published:2022-09-07 Issue: Volume: Page:
ISSN:
Container-title:2022 7th International Conference on Frontiers of Signal Processing (ICFSP)
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Author:

Zerrouki Nabil¹,Harrou Fouzi²,Sun Ying²,Djafer Amina Zouina Ait¹,Amrane Houacine³

Affiliation:

1. Center for Development of Advanced Technologies,Design and Implementation of Intelligent Machines (DIIM) Team,Baba Hassen,Algeria

2. King Abdullah University of Science and Technology (KAUST),CEMSE Division,Thuwal 23955-6900,Saudi Arabia

3. University of Sciences and Technology Houari Boumédienne (USTHB),LCPTS, Faculty of Electronics and Computer Science,Algiers,Algeria

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9924548/9924621/09924708.pdf?arnumber=9924708

Cited by 5 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. Enhancing Detection of Falls and Bed-Falls Using a Depth Sensor and Convolutional Neural Network;IEEE Sensors Journal;2024-07-15

2. Image-driven machine learning for automatic characterization of grain size and distribution in smart vanadium dioxide thin films;Measurement;2024-06

3. A robust fall detection approach based on 4D imaging millimeter-wave radar;Fifteenth International Conference on Signal Processing Systems (ICSPS 2023);2024-03-28

4. A Fall Detection System Based on FMCW Radar Range-Doppler Image and Bi-LSTM Deep Learning;IEEE Sensors Journal;2023-09-15

5. Secure Your Steps: A Class-Based Ensemble Framework for Real-Time Fall Detection Using Deep Neural Networks;IEEE Access;2023

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