HRNN4F: hybrid deep random neural network for multi-channel fall activity detection

Ahsen Tahir*, Jawad Ahmad, Gordon Morison, Hadi Larijani, Ryan M. Gibson, Dawn A. Skelton

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
233 Downloads (Pure)


Falls are a major health concern in older adults. Falls lead to mortality, immobility and high costs to social and health care services. Early detection and classification of falls is imperative for timely and appropriate medical aid response. Traditional machine learning models have been explored for fall classification. While newly developed deep learning techniques have the ability to potentially extract high-level features from raw sensor data providing high accuracy and robustness to variations in sensor position, orientation and diversity of work environments that may skew traditional classification models. However, frequently used deep learning models like Convolutional Neural Networks (CNN) are computationally intensive. To the best of our knowledge, we present the first instance of a Hybrid Multichannel Random Neural Network (HMCRNN) architecture for fall detection and classification. The proposed architecture provides the highest accuracy of 92.23% with dropout regularization, compared to other deep learning implementations. The performance of the proposed technique is approximately comparable to a CNN yet requires only half the computation cost of the CNN-based implementation. Furthermore, the proposed HMCRNN architecture provides 34.12% improvement in accuracy on average than a Multilayer Perceptron.
Original languageEnglish
Pages (from-to)37-50
Number of pages14
JournalProbability in the Engineering and Informational Sciences
Issue number1
Early online date23 Aug 2019
Publication statusPublished - Jan 2021


  • probabilistic networks
  • queuing theory
  • simulation

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Management Science and Operations Research


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