Classification of indoor human fall events using deep learning

Arifa Sultana, Kaushik Deb, Pranab Kumar Dhar, Takeshi Koshiba

Research output: Contribution to journalArticlepeer-review

Abstract

Human fall identification can play a significant role in generating sensor based alarm systems, assisting physical therapists not only to reduce after fall effects but also to save human lives. Usually, elderly people suffer from various kinds of diseases and fall action is a very frequently occurring circumstance at this time for them. In this regard, this paper represents an architecture to classify fall events from others indoor natural activities of human beings. Video frame generator is applied to extract frame from video clips. Initially, a two dimensional convolutional neural network (2DCNN) model is proposed to extract features from video frames. Afterward, gated recurrent unit (GRU) network finds the temporal dependency of human movement. Binary cross-entropy loss function is calculated to update the attributes of the network like weights, learning rate to minimize the losses. Finally, sigmoid classifier is used for binary classification to detect human fall events. Experimental result shows that the proposed model obtains an accuracy of 99%, which outperforms other state-of-the-art models.

Original languageEnglish
Article number328
Pages (from-to)1-20
Number of pages20
JournalEntropy
Volume23
Issue number3
DOIs
Publication statusPublished - 2021 Mar

Keywords

  • Convolutional neural network (CNN)
  • Deep learning
  • Gated recurrent unit (GRU)
  • Human fall classification
  • Recurrent neural network (RNN)

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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