Comparison of Fall Detection Systems Based on YOLOPose and Long Short-Term Memory
Author:
Affiliation:
1. ICT & Robotics Engineering and IITC, Hankyong National University, Anseong 17579, Republic of Korea
2. Bundang Convergence Technology Campus of Korea Polytechnic, Seongnam 13590, Republic of Korea
Funder
National Research Foundation of Korea
Ministry of Education
Publisher
Korea Institute of Information and Communication Engineering
Link
https://pdf.medrang.co.kr/JICCE/2024/022/JICCE022-02-139.pdf
Reference28 articles.
1. World Health Organization. Available online: https://www.who.int/news-room/fact-sheets/detail/falls (accessed on 26 April 2021).
2. National Health Administration, Ministry of Health and Welfare. Available online: https://www.hpa.gov.tw/Pages/Detail.aspx?nodeid=807&pid=4326 (accessed on 9 March 2020).
3. User Verification Leveraging Gait Recognition for Smartphone Enabled Mobile Healthcare Systems
4. A Survey on Recent Advances in Wearable Fall Detection Systems
5. A Study on the Application of Convolutional Neural Networks to Fall Detection Evaluated with Multiple Public Datasets
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