Efficient k-NN implementation for real-time detection of cough events in smartphones

Carlos Hoyos Barceló, Jesús Monge-Álvarez, Muhammad Zeeshan Shakir, Jose M. Alcaraz Calero, Juan Pablo Casaseca

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)
267 Downloads (Pure)


The potential of telemedicine in respiratory health care has not been completely unveiled in part due to the inexistence of reliable objective measurements of symptoms such as cough. Currently available cough detectors are uncomfortable and expensive at a time when generic smartphones can perform this task. However, two major challenges preclude smartphone-based cough detectors from effective deployment namely, the need to deal with noisy environments and computational cost. This paper focuses on the latter, since complex machine learning algorithms are too slow for real-time use and kill the battery in a few hours unless specific actions are taken. In this paper, we present a robust and efficient implementation of a smartphone-based cough detector. The audio signal acquired from the device’s microphone is processed by computing local Hu moments as a robust feature set in the presence of background noise. We previously demonstrated that pairing Hu moments and a standard k-NN classifier achieved accurate cough detection at the expense of computation time. To speed-up k-NN search, many tree structures have been proposed. Our cough detector uses an improved vp-tree with optimized construction methods and a distance function that results in faster searches. We achieve 18x speed-up over classic vp-trees, and 560x over standard implementations of k-NN in state-of-the-art machine learning libraries, with classification accuracies over 93%, enabling real-time performance on low-end smartphones.
Original languageEnglish
Pages (from-to)1672-1771
Number of pages10
JournalIEEE Journal of Biomedical and Health Informatics
Issue number5
Publication statusPublished - 2 Nov 2017


Dive into the research topics of 'Efficient k-NN implementation for real-time detection of cough events in smartphones'. Together they form a unique fingerprint.

Cite this