Fall Detection Using Neural Network Based on Internet of Things Streaming Data
Zana Azeez Kakarash, Sarkhel H. Taher Karim, Mokhtar Mohammadi · UHD Journal of Science and Technology · 2020
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.