Automatic Classification of Volumes of Water Using Swallow Sounds from Cervical Auscultation

Subramani, Siddharth; Rao, M V Achuth; Giridhar, Divya; Hegde, Prasanna Suresh; Ghosh, Prasanta Kumar

doi:10.1109/icassp40776.2020.9053037

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…The acoustic signal is collected by a microphone placed in the right ear. Firstly, we down-sample the acoustic signal from 44.1 kHz to 16 kHz [29]. Then, a spectrogram-based denoising method is utilized [30].…”

Section: Acoustic Signalsmentioning

confidence: 99%

Multi-Sensor Fusion Approach to Drinking Activity Identification for Improving Fluid Intake Monitoring

Li,

Yu,

Wang

et al. 2024

Applied Sciences

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People nowadays often ignore the importance of proper hydration. Water is indispensable to the human body’s function, including maintaining normal temperature, getting rid of wastes and preventing kidney damage. Once the fluid intake is lower than the consumption, it is difficult to metabolize waste. Furthermore, insufficient fluid intake can also cause headaches, dizziness and fatigue. Fluid intake monitoring plays an important role in preventing dehydration. In this study, we propose a multimodal approach to drinking activity identification to improve fluid intake monitoring. The movement signals of the wrist and container, as well as acoustic signals of swallowing, are acquired. After pre-processing and feature extraction, typical machine learning algorithms are used to determine whether each sliding window is a drinking activity. Next, the recognition performance of the single-modal and multimodal methods is compared through the event-based and sample-based evaluation. In sample-based evaluation, the proposed multi-sensor fusion approach performs better on support vector machine and extreme gradient boosting and achieves 83.7% and 83.9% F1-score, respectively. Similarly, the proposed method in the event-based evaluation achieves the best F1-score of 96.5% on the support vector machine. The results demonstrate that the multimodal approach performs better than the single-modal in drinking activity identification.

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Section: Acoustic Signalsmentioning

confidence: 99%

Multi-Sensor Fusion Approach to Drinking Activity Identification for Improving Fluid Intake Monitoring

Li,

Yu,

Wang

et al. 2024

Applied Sciences

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“…They concluded that swallow sound contains audible cues that can be used for reliable classification of dysphagia. Interestingly, Subramani et al (2020) successfully classified swallowing sounds associated with different volumes based on an array of acoustic features obtained from cervical auscultation using machine learning (Subramani et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Interestingly, Subramani et al. (2020) successfully classified swallowing sounds associated with different volumes based on an array of acoustic features obtained from cervical auscultation using machine learning (Subramani et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

Acoustic characteristics associated with liquid swallowing sounds of different bolus consistencies in young healthy adults

2022

Intl J Lang & Comm Disor

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Background: Cervical auscultation has been used clinically as an augmentative procedure for swallow examination. Despite its frequent use for screening and preliminary assessment of swallowing, the usefulness of cervical auscultation is controversial due to a lack of sufficient research, particularly in quantifying swallowing sounds. Aims:To document the acoustic characteristics of normal swallowing sound associated with swallowing bolus of different consistencies among younger healthy adults. Methods & Procedures: A total of 30 healthy young adult participants swallowed 5 ml thickened liquids of four different consistencies (slightly thick, mildly thick, moderately thick and extremely thick) prepared using a starchbased commercial thickener, and the corresponding swallowing sounds were recorded using a wireless accelerometric stethoscope. An array of acoustic measures including duration of swallowing sound (DSS), duration to peak intensity (DPI), frequency at peak intensity (FPI), peak intensity (PI), average intensity (AI), and difference between peak and average intensity (DPAI) values associated with the swallowing signals were obtained. Outcomes & Results:In general, increased durational measures of the swallowing sounds were associated with an increase in bolus consistency. Intensity measures including PI, AI and DPAI were found to be stable across different consistencies. The change in FPI did not appear to be particularly meaningful due to its high variability. In addition, no significant differences were observed between men and women.Conclusions & Implications: Swallow sounds associated with different bolus consistencies could be quantified and used to differentiate consistencies. The present findings could serve as a reference for future swallowing research of normal and dysphagic population.

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Mealcoach: Contact Microphone-Based Meal Supervision For Post-Stroke Dysphagia Patients

Gong,

Qiao,

Huang

et al. 2023

2023 45th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Automatic Classification of Volumes of Water Using Swallow Sounds from Cervical Auscultation

Cited by 4 publications

References 15 publications

Multi-Sensor Fusion Approach to Drinking Activity Identification for Improving Fluid Intake Monitoring

Multi-Sensor Fusion Approach to Drinking Activity Identification for Improving Fluid Intake Monitoring

Acoustic characteristics associated with liquid swallowing sounds of different bolus consistencies in young healthy adults

Mealcoach: Contact Microphone-Based Meal Supervision For Post-Stroke Dysphagia Patients

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