Fall Detection Using Neural Network Based on Internet of Things Streaming Data

UHD Journal of Science and Technology

Sarkhel H.Taher Karim¹  Zana Azeez Kakarash²  Mokhtar Mohammadi³ 

[1] Department of Computer Science, College of Science, University of Halabja, Halabja, Iraq, Department of Computer Network, Technical College of Informatics, Sulaimani Polytechnic University, Sulaymaniyah, Iraq,;Department of Engineering, Faculty of Engineering and Computer Science, Qaiwan International University, Sulaymaniyah, Iraq, Department of Computer Engineering and Information Technology, Faculty of Engineering, Razi University, Kermanshah, Iran,;Department of Information Technology, Lebanese French University, Erbil, Kurdistan Region, Iraq;

关键词: fall detection;    internet of things;    artificial neural networks;    machine learning;   

DOI  :  https://doi.org/10.21928/uhdjst.v4n2y2020.pp91-98

来源: DOAJ

【 摘 要 】

Fall event has become a critical health problem among elderly people. We propose a fall detection system that analyzes real-time streaming data from the Internet of Things (IoT) to detect irregular patterns related to fall. We train a deep neural network model using accelerometer data from an online physical activity monitoring dataset named, MobiAct. An IBM Cloud-based IoT data processing framework is used to manage streaming data. About 96.71% of accuracy is achieved in assessing the performance of the proposed model.

【 授权许可】

Unknown