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


  • Zana Azeez Kakarash 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,
  • Sarkhel H.Taher Karim 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,
  • Mokhtar Mohammadi Department of Information Technology, Lebanese French University, Erbil, Kurdistan Region, Iraq




Fall Detection, Internet of Things, Artificial Neural Networks, Machine Learning


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.


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