Towards a Wearable Cough Detector Based on Neural Networks

Kadambi, Prad; Mohanty, Abinash; Ren, Hao; Smith, Jaclyn A.; McGuinnes, Kevin; Holt, Kimberley; Furtwaengler, Armin; Slepetys, Roberto; Zheng, Yang; Seo, Jae-sun; Chae, Junseok; Cao, Yu; Berisha, Visar

doi:10.1109/icassp.2018.8461394

Cited by 19 publications

(16 citation statements)

References 14 publications

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“…For instance, MobiCough [27] used a collar based microphone to collect their data. Similarly, [11] and [28] used a neck-worn device to record audio. While these ideally placed devices are better suited to pick up cough sounds, they are less practical for long term use.…”

Section: Related Workmentioning

confidence: 99%

Coughwatch: Real-World Cough Detection using Smartwatches

Liaqat

Chen

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Continuous monitoring of cough may provide insights into the health of individuals as well as the effectiveness of treatments. Smartwatches, in particular, are highly promising for such monitoring: they are inexpensive, unobtrusive, programmable, and have a variety of sensors. However, current mobile cough detection systems are not designed for smartwatches, and perform poorly when applied to real-world smartwatch data since they are often evaluated on data collected in the lab.In this work we propose CoughWatch, a lightweight cough detector for smartwatches that uses audio and movement data for inthe-wild cough detection. On our in-the-wild data, CoughWatch achieves a precision of 82% and recall of 55%, compared to 6% precision and 19% recall achieved by the current state-of-the-art approach. Furthermore, by incorporating gyroscope and accelerometer data, CoughWatch improves precision by up to 15.5 percentage points compared to an audio-only model.

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Section: Related Workmentioning

confidence: 99%

Coughwatch: Real-World Cough Detection using Smartwatches

Liaqat

Chen

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…P Kadambi et al determined 12 MFCCs and delta delta highlights of a hacking and non-hacking sound sign [43,44]. The highlights are utilized to prepare three layered ANN for paired grouping.…”

Section: A Machine Learning On Sound Time Seriesmentioning

confidence: 99%

Deep Learning and Internet of Things Based Lung Ailment Recognition Through Coughing Spectrograms

et al. 2021

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Coughing analysis stays a region that has gotten meager consideration from AI scientists. This can be credited to a few factors, for example, wasteful auxiliary frameworks, high costs in getting databases, or trouble in building classifiers. The current paper classifies and audits the advancement on coughing sound investigation, AI models, and the information assortment strategies through IoT (Internet of Things) for the grouping of pulmonary sicknesses. Moreover, it proposes a Multi-layered Convolutional Neural Network (Deep Convolutional Neural Network-DCNN) for the arrangement of eight pneumonic infections. The DCNN utilizes otherworldly highlights, cepstral coefficients, chroma highlights, and spectrograms from coughing sound for preparing. To test the viability of the model, a similar report with four standard models was directed on a database of 112 patients gathered from a pediatric office in India through a cloud server and wearable electronic sensors. Results demonstrated that the proposed model accomplished an accuracy of 0.4 on the test segment, which was practically equivalent to recent models proposed in the writing overviewed. INDEX TERMSWearable electronic sensors, Internet of Things, Cloud Computing, Coughing spectrograms, Spectral and Chroma features, Cepstral Coefficients. I. BACKGROUND VOLUME 4

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“…where R represent the newly defined energy ratio. Here, a single value for loudness and the energy ratio was derived from the frame at 0.05 s (starting point of cough and calculation), which was carried out while moving the frame to overlap 75% to derive characteristics according to cough duration [27,28]. H rms is the root mean-square (RMS) value of the high-frequency component and L rms is the RMS value of the low-frequency component.…”

Section: Extraction Of Acoustic Features Of Cough Soundsmentioning

confidence: 99%

Diagnosis of Pneumonia by Cough Sounds Analyzed with Statistical Features and AI

Chung

Jin

et al. 2021

Sensors

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Pneumonia is a serious disease often accompanied by complications, sometimes leading to death. Unfortunately, diagnosis of pneumonia is frequently delayed until physical and radiologic examinations are performed. Diagnosing pneumonia with cough sounds would be advantageous as a non-invasive test that could be performed outside a hospital. We aimed to develop an artificial intelligence (AI)-based pneumonia diagnostic algorithm. We collected cough sounds from thirty adult patients with pneumonia or the other causative diseases of cough. To quantify the cough sounds, loudness and energy ratio were used to represent the level and its spectral variations. These two features were used for constructing the diagnostic algorithm. To estimate the performance of developed algorithm, we assessed the diagnostic accuracy by comparing with the diagnosis by pulmonologists based on cough sound alone. The algorithm showed 90.0% sensitivity, 78.6% specificity and 84.9% overall accuracy for the 70 cases of cough sound in pneumonia group and 56 cases in non-pneumonia group. For same cases, pulmonologists correctly diagnosed the cough sounds with 56.4% accuracy. These findings showed that the proposed AI algorithm has value as an effective assistant technology to diagnose adult pneumonia patients with significant reliability.

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Towards a Wearable Cough Detector Based on Neural Networks

Cited by 19 publications

References 14 publications

Coughwatch: Real-World Cough Detection using Smartwatches

Coughwatch: Real-World Cough Detection using Smartwatches

Deep Learning and Internet of Things Based Lung Ailment Recognition Through Coughing Spectrograms

Diagnosis of Pneumonia by Cough Sounds Analyzed with Statistical Features and AI

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