Efficient computation of image moments for robust cough detection using smartphones

Carlos Hoyos Barceló, Jesús Monge-Álvarez, Zeeshan Pervez, Luis M. San-Jos´e-Revuelta, Juan Pablo Casaseca

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)
239 Downloads (Pure)


Health Monitoring apps for smartphones have the potential to improve quality of life and decrease the cost of health services. However, they have failed to live up to expectation in the context of respiratory disease. This is in part due to poor objective measurements of symptoms such as cough. Real-time cough detection using smartphones faces two main challenges namely, the necessity of dealing with noisy input signals, and the need of the algorithms to be computationally efficient, since a high battery consumption would prevent patients from using them. This paper proposes a robust and efficient smartphone-based cough detection system able to keep the phone battery consumption below 25% (16% if only the detector is considered) during 24h use. The proposed system efficiently calculates local image moments over audio spectrograms to feed an optimized classifier for final cough detection. Our system achieves 88:94% sensitivity and 98:64% specificity in noisy environments with a 5500 speed-up and 4 battery saving compared to the baseline implementation. Power consumption is also reduced by a minimum factor of 6 compared to existing optimized systems in the literature.
Original languageEnglish
Pages (from-to)176-185
Number of pages10
JournalComputers in Biology and Medicine
Early online date17 Jul 2018
Publication statusPublished - 1 Sept 2018


  • Event detection
  • Cough detection
  • Mobile health
  • Moment theory
  • Optimization


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