Breathing-Based Authentication on Resource-Constrained IoT Devices using Recurrent Neural Networks

Research output: Contribution to journalArticleScientificpeer-review


  • Jagmohan Chauhan
  • Suranga Seneviratne
  • Yining Hu
  • Archan Misra
  • Aruna Seneviratne
  • Youngki Lee

Research units

  • University of New South Wales
  • Singapore Management University


Recurrent neural networks (RNNs) have shown promising results in audio and speech-processing applications. The increasing popularity of Internet of Things (IoT) devices makes a strong case for implementing RNN-based inferences for applications such as acoustics-based authentication and voice commands for smart homes. However, the feasibility and performance of these inferences on resource-constrained devices remain largely unexplored. The authors compare traditional machine-learning models with deep-learning RNN models for an end-to-end authentication system based on breathing acoustics.


Original languageEnglish
Pages (from-to)60-67
Number of pages8
Issue number5
Publication statusPublished - May 2018
MoE publication typeA1 Journal article-refereed

ID: 27671943