A novel random neural network-based fall activity recognition

Syed Yaseen Shah, Hadi Larijani, Ryan Gibson, Dimitrios Liarokapis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


The past few decades have witnessed a sharp increase in life expectancy. As a result, the proportion of elderly people is increasing worldwide. Consequently, Dementia and Parkinson's disease are expected to rise, thereby increasing the risk of critical events such as falls for elderly people. This has prompted many researchers to develop a wide range of solutions for fall detection and prevention. However, these solutions are either inaccurate or impractical due to hardware complexity. In this paper, we have proposed a novel Random Neural Network (RNN) based fall detection scheme. Results obtained from the proposed RNN-based scheme are compared with traditional machine learning methods such as Support Vector Machine (SVM) and traditional Artificial Neural Network (ANN) etc. From the results, it is evident that the proposed scheme has a higher accuracy of 98%. Additionally, several other parameters such as precision, recall, specificity, and F-measure show that the proposed algorithm has better generalisation capabilities when compared with other traditional machine learning schemes. Furthermore, the proposed RNN is also compared with a recent scheme and the obtained results demonstrate the superiority of the proposed scheme.
Original languageEnglish
Title of host publication2020 International Conference on UK-China Emerging Technologies, UCET 2020
Number of pages4
ISBN (Electronic)9781728194882
ISBN (Print)9781728194899
Publication statusPublished - 29 Sept 2020
Event5th International Conference on the UK-China Emerging Technologies (UCET) 2020 - Online
Duration: 20 Aug 202021 Aug 2020
https://www.gla.ac.uk/events/conferences/ucet/ (Link to conference website)


Conference5th International Conference on the UK-China Emerging Technologies (UCET) 2020
Abbreviated titleUCET
Internet address


  • fall detection, remote healthcare, neural networks, machine learning, RNN
  • RNN
  • Neural Networks
  • Remote Healthcare
  • Fall detection
  • Machine Learning

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Information Systems and Management
  • Engineering (miscellaneous)


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