Performance enhancement of vision based fall detection using ensemble of machine learning model-Reference-Cited by-同舟云学术

Performance enhancement of vision based fall detection using ensemble of machine learning model

Published:2022-11-30 Issue: Volume: Page:
ISSN:1386-7857
Container-title:Cluster Computing
language:en
Short-container-title:Cluster Comput

Author:

Rastogi Shikha,Singh Jaspreet

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Software

Link

https://link.springer.com/content/pdf/10.1007/s10586-022-03818-6.pdf

Reference38 articles.

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2. Xiong, X., Min, W., Zheng, W.S., Liao, P., Yang, H., Wang, S.: S3D-CNN: skeleton-based 3D consecutive-low-pooling neural network for fall detection. Appl. Intell. 50(10), 3521–3534 (2020)

3. Li, H., Li, C., Ding, Y.: Fall detection based on fused saliency maps. Multimed. Tools Appl. 80(2), 1883–1900 (2021)

4. Khraief, C., Benzarti, F., Amiri, H.: Elderly fall detection based on multi-stream deep convolutional networks. Multimed. Tools Appl. 79, 19537 (2020)

5. Mehta, V., Dhall, A., Pal, S., Khan, S.: Motion and region aware adversarial learning for fall detection with thermal imaging. https://arxiv.org/abs/2004.08352 (2020)

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1. Federated Learning Based Elderly Fall Detection Using Edge Computing;2023 International Conference on Next Generation Electronics (NEleX);2023-12-14

2. A Novel Approach for Fall Detection Using Thermal Imaging and a Stacking Ensemble of Autoencoder and 3D-CNN Models;2023 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE);2023-09-24