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

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Abstract

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)1-10
Number of pages10
JournalIEEE Journal of Biomedical and Health Informatics
DOIs
Publication statusPublished - 2 Nov 2017

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Smartphones
Cough
Detectors
Learning systems
Telemedicine
Bioelectric potentials
Microphones
Health care
Learning algorithms
Classifiers
Libraries
Noise
Smartphone
Delivery of Health Care
Costs
Costs and Cost Analysis
Equipment and Supplies

Cite this

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title = "Efficient k-NN implementation for real-time detection of cough events in smartphones",
abstract = "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.",
author = "{Hoyos Barcel{\'o}}, Carlos and Jes{\'u}s Monge-{\'A}lvarez and Shakir, {Muhammad Zeeshan} and {Alcaraz Calero}, {Jose M.} and Casaseca, {Juan Pablo}",
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AU - Casaseca, Juan Pablo

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