Effect of downsampling and compressive sensing on audio-based continuous cough monitoring

Casaseca‐de‐la‐Higuera, Pablo; Lesso, Paul; McKinstry, Brian; Pinnock, Hilary; Rabinovich, Roberto; McCloughan, Lucy; Monge-Álvarez, Jesús

doi:10.1109/embc.2015.7319816

Cited by 11 publications

(11 citation statements)

References 10 publications

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“…This approach is relatively non-intrusive, and can be completely non-contact, requiring only the presence of an acoustic sensing device within a reasonable proximity (primarily governed by the level of background noise) and a device to facilitate the requisite signal processing. Some implementations have demonstrated use of a contact microphone [ 162 , 163 , 164 , 165 ]; however, many modern approaches have adopted the ubiquitous smart-phone for audio capture (and in some instances, also processing) device [ 166 , 167 , 168 , 169 ]. A cough event lasts somewhere between 300 [ 170 ] and 650 ms [ 171 ], and can be described by three distinct phases: the expulsive phase, the intermediate phase, and the voiced phase [ 172 ].…”

Section: Acoustic Detection Of Respiratory Infectionmentioning

confidence: 99%

Noncontact Sensing of Contagion

Khanam

Chahl

Chahl³

et al. 2021

J. Imaging

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The World Health Organization (WHO) has declared COVID-19 a pandemic. We review and reduce the clinical literature on diagnosis of COVID-19 through symptoms that might be remotely detected as of early May 2020. Vital signs associated with respiratory distress and fever, coughing, and visible infections have been reported. Fever screening by temperature monitoring is currently popular. However, improved noncontact detection is sought. Vital signs including heart rate and respiratory rate are affected by the condition. Cough, fatigue, and visible infections are also reported as common symptoms. There are non-contact methods for measuring vital signs remotely that have been shown to have acceptable accuracy, reliability, and practicality in some settings. Each has its pros and cons and may perform well in some challenges but be inadequate in others. Our review shows that visible spectrum and thermal spectrum cameras offer the best options for truly noncontact sensing of those studied to date, thermal cameras due to their potential to measure all likely symptoms on a single camera, especially temperature, and video cameras due to their availability, cost, adaptability, and compatibility. Substantial supply chain disruptions during the pandemic and the widespread nature of the problem means that cost-effectiveness and availability are important considerations.

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Section: Acoustic Detection Of Respiratory Infectionmentioning

confidence: 99%

Noncontact Sensing of Contagion

Khanam

Chahl

Chahl³

et al. 2021

J. Imaging

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“…The aim of this audio corpus was to record various voluntary cough sounds, but also some other sounds identified in the literature as typical examples of sounds that are confused with cough [24], [31]. Voluntary coughs have been used in previous literature to show the feasibility of cough monitors [18], [19], [24], [32] or objectively evaluate the performance of several sensors for cough detection [31]. The lab setting followed a similar set-up as in [31], where a person is sitting (in a quiet environment) at a table on which the recording device is placed.…”

Section: A Data Collectionmentioning

confidence: 99%

“…The obvious limitation of this study is that only voluntary coughs were used to examine the performance of the different approaches across devices. Even if voluntary coughs have already been used to show the feasibility of cough monitors [18], [19], [24], [32], reflex coughs would further allow a disease specific analysis of the performance values. Another limitation stems from the laboratory setting of the study, which limits the generalizability of the performance metrics.…”

Section: Limitationsmentioning

confidence: 99%

Towards Device-Agnostic Mobile Cough Detection with Convolutional Neural Networks

Barata

Kipfer

Weber

et al. 2019

2019 IEEE International Conference on Healthcare Informatics (ICHI)

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Ubiquitous mobile devices have the potential to reduce the financial burden of healthcare systems by providing scalable and cost-efficient health monitoring applications. Coughing is a symptom associated with prevalent pulmonary diseases, and bears great potential for being exploited by monitoring applications. Prior research has shown the feasibility of cough detection by smartphone-based audio recordings, but it is still open as to whether current detection models generalize well to a variety of mobile devices to ensure scalability. We first conducted a lab study with 43 subjects and recorded 6737 cough samples and 8854 control sounds by 5 different recording devices. We then reimplemented two approaches from prior work and investigated their performance in two different scenarios across devices. We propose an efficient convolutional neural network architecture and an ensemble based classifier to reduce the cross-device discrepancy. Our approach produced mean accuracies in the range [85.9%, 90.9%], showing consistency across devices (SD = [1.5%, 2.7%]) and outperforming prior learning algorithms. Thus, our proposal is a step towards cost-efficient, ubiquitous, scalable and device-agnostic cough detection.

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“…1. The audio signal is downsampled at 8.82 kHz, which has shown appropriate for cough detection [29]. The samples are then grouped in 50 ms windows (441 samples) with 25ms shift.…”

Section: Overall System Overviewmentioning

confidence: 99%

Efficient computation of image moments for robust cough detection using smartphones

Hoyos-Barceló

Monge-Álvarez

Pervez

et al. 2018

Computers in Biology and Medicine

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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 24 h 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.

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Effect of downsampling and compressive sensing on audio-based continuous cough monitoring

Cited by 11 publications

References 10 publications

Noncontact Sensing of Contagion

Noncontact Sensing of Contagion

Towards Device-Agnostic Mobile Cough Detection with Convolutional Neural Networks

Efficient computation of image moments for robust cough detection using smartphones

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