Nocturnal Cough and Snore Detection in Noisy Environments Using Smartphone-Microphones

Vhaduri, Sudip; Kessel, Theodore van; Ko, Bongjun; Wood, David; Wang, Shiqiang; Brunschwiler, Thomas

doi:10.1109/ichi.2019.8904563

Cited by 54 publications

(21 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…have investigated cough and snore detection in the presence of ambient room noise using smartphones. The k-NN classifier resulted in the highest accuracy (97%) when performing the binary classification between cough and snore events [32]. Voluntary cough detection against speech in a population of volunteers has also been investigated [33].…”

Section: Cough Detectionmentioning

confidence: 99%

Novel Coronavirus Cough Database: NoCoCoDa

2020

View full text Add to dashboard Cite

The current pandemic associated with the novel coronavirus (COVID-19) presents a new area of research with its own set of challenges. Creating unobtrusive remote monitoring tools for medical professionals that may aid in diagnosis, monitoring and contact tracing could lead to more efficient and accurate treatments, especially in this time of physical distancing. Audio based sensing methods can address this by measuring the frequency, severity and characteristics of the COVID-19 cough. However, the feasibility of accumulating coughs directly from patients is low in the short term. This paper introduces a novel database (NoCoCoDa), which contains COVID-19 cough events obtained through public media interviews with COVID-19 patients, as an interim solution. After manual segmentation of the interviews, a total of 73 individual cough events were extracted and cough phase annotation was performed. Furthermore, the COVID-19 cough is typically dry but can present as a more productive cough in severe cases. Therefore, an investigation of cough sub-type (productive vs. dry) of the NoCoCoDa was performed using methods previously published by our research group. Most of the NoCoCoDa cough events were recorded either during or after a severe period of the disease, which is supported by the fact that 77% of the COVID-19 coughs were classified as productive based on our previous work. The NoCoCoDa is designed to be used for rapid exploration and algorithm development, which can then be applied to more extensive datasets and potentially real time applications. The NoCoCoDa is available for free to the research community upon request. INDEX TERMS Acoustic signal processing, audio databases, audio systems, biomedical measurement, biomedical monitoring, data analysis, data collection, medical conditions, medical diagnosis, patient monitoring, smart homes.

show abstract

Section: Cough Detectionmentioning

confidence: 99%

Novel Coronavirus Cough Database: NoCoCoDa

2020

View full text Add to dashboard Cite

show abstract

“…For instance, videos and images obtained from smartphone camera or data screened through installed inertial sensors were studied for detecting human fatigue levels (Karvekar 2019 ; Roldán Jiménez et al 2019 ). Likewise, condition of cough can be detected from the audio recorded through smartphone microphone (Nemati et al 2019 ; Vhaduri et al 2019 ). Even, the video obtained through smartphone can assist in the prediction of nausea (Story et al 2019 ).…”

Section: Artificial Intelligencementioning

confidence: 99%

Smart technologies driven approaches to tackle COVID-19 pandemic: a review

Khan

Kushwah²,

Singh³

et al. 2021

3 Biotech

View full text Add to dashboard Cite

“…For example, Hristo et al [ 41 ] develop a mobile device fingerprinting system based on the k -NN algorithm. Additionally, Sudip et al [ 76 ] develop a smartphone-based health monitoring system using the k -NN model. A smartphone-based data mining system is presented in [ 60 ], for fall detection using a k -NN algorithm.…”

Section: Embedded Machine Learning Techniquesmentioning

confidence: 99%

An Overview of Machine Learning within Embedded and Mobile Devices–Optimizations and Applications

Ajani

Imoize

Atayero

2021

Sensors

105

View full text Add to dashboard Cite

Embedded systems technology is undergoing a phase of transformation owing to the novel advancements in computer architecture and the breakthroughs in machine learning applications. The areas of applications of embedded machine learning (EML) include accurate computer vision schemes, reliable speech recognition, innovative healthcare, robotics, and more. However, there exists a critical drawback in the efficient implementation of ML algorithms targeting embedded applications. Machine learning algorithms are generally computationally and memory intensive, making them unsuitable for resource-constrained environments such as embedded and mobile devices. In order to efficiently implement these compute and memory-intensive algorithms within the embedded and mobile computing space, innovative optimization techniques are required at the algorithm and hardware levels. To this end, this survey aims at exploring current research trends within this circumference. First, we present a brief overview of compute intensive machine learning algorithms such as hidden Markov models (HMM), k-nearest neighbors (k-NNs), support vector machines (SVMs), Gaussian mixture models (GMMs), and deep neural networks (DNNs). Furthermore, we consider different optimization techniques currently adopted to squeeze these computational and memory-intensive algorithms within resource-limited embedded and mobile environments. Additionally, we discuss the implementation of these algorithms in microcontroller units, mobile devices, and hardware accelerators. Conclusively, we give a comprehensive overview of key application areas of EML technology, point out key research directions and highlight key take-away lessons for future research exploration in the embedded machine learning domain.

show abstract

Nocturnal Cough and Snore Detection in Noisy Environments Using Smartphone-Microphones

Cited by 54 publications

References 19 publications

Novel Coronavirus Cough Database: NoCoCoDa

Novel Coronavirus Cough Database: NoCoCoDa

Smart technologies driven approaches to tackle COVID-19 pandemic: a review

An Overview of Machine Learning within Embedded and Mobile Devices–Optimizations and Applications

Contact Info

Product

Resources

About