Maximizing Accuracy of Fall Detection and Alert Systems Based on 3D Convolutional Neural Network-Reference-Cited by-同舟云学术

Maximizing Accuracy of Fall Detection and Alert Systems Based on 3D Convolutional Neural Network

Published:2017-04-18 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Second International Conference on Internet-of-Things Design and Implementation
language:
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Author:

Hwang Seokhyun¹,Ahn DaeHan²,Park Homin¹,Park Taejoon¹

Affiliation:

1. Hanyang University

2. Daegu Gyeongbuk Institute of Science and Technology

Funder

Ministry of Science, ICT and Future Planning

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3054977.3057314

Reference4 articles.

1. Xin Ma Haibo Wang Bingxia Xue Mingang Zhou Bing Ji and Yibin Li. 2014. Depth-based human fall detection via shape features and improved extreme learning machine. IEEE journal of biomedical and health informatics 18 6 (2014) 1915--1922. Xin Ma Haibo Wang Bingxia Xue Mingang Zhou Bing Ji and Yibin Li. 2014. Depth-based human fall detection via shape features and improved extreme learning machine. IEEE journal of biomedical and health informatics 18 6 (2014) 1915--1922.

2. A Study on Human Fall Detection Systems: Daily Activity Classification and Sensing Techniques;Nizam Yoosuf;International Journal of Integrated Engineering,2016

3. FADES: Behavioral detection of falls using body shapes from 3D joint data

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