AFAR: A Real-Time Vision-based Activity Monitoring and Fall Detection Framework using 1D Convolutional Neural Networks-Reference-Cited by-同舟云学术

AFAR: A Real-Time Vision-based Activity Monitoring and Fall Detection Framework using 1D Convolutional Neural Networks

Published:2022-02-18 Issue: Volume: Page:
ISSN:
Container-title:2022 14th International Conference on Machine Learning and Computing (ICMLC)
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Author:

Suarez Jessie James Profeta¹,Orillaza Nathaniel²,Naval Prospero¹

Affiliation:

1. University of the Philippines, Diliman, Department of Computer Science, Philippines

2. University of the Philippines, Manila, College of Medicine, Philippines

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3529836.3529862

Reference32 articles.

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3. Silhouette Orientation Volumes for Efficient Fall Detection in Depth Videos

4. Valentin Bazarevsky Ivan Grishchenko Karthik Raveendran Tyler Zhu Fan Zhang and Matthias Grundmann. 2020. BlazePose: On-device Real-time Body Pose tracking. arXiv preprint arXiv:2006.10204(2020). Valentin Bazarevsky Ivan Grishchenko Karthik Raveendran Tyler Zhu Fan Zhang and Matthias Grundmann. 2020. BlazePose: On-device Real-time Body Pose tracking. arXiv preprint arXiv:2006.10204(2020).

5. A novel approach for fall detection in home environment

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4. Advances in Skeleton-Based Fall Detection in RGB Videos: From Handcrafted to Deep Learning Approaches;IEEE Access;2023

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