CoughBuddy: Multi-Modal Cough Event Detection Using Earbuds Platform
Abstract
There has been an extensive amount of study on cough detection using acoustic features captured from smartphones and smartwatches in the past decade. However, the specificity of the algorithms has always been a concern when exposed to the unseen field data containing cough-like sounds. In this paper, we propose a novel sensor fusion algorithm that employs a hybrid of classification and template matching algorithms to tackle the problem of unseen classes. The algorithm utilizes in-ear audio signal as well as head motion captured by the inertial measurement unit (IMU). A clinical study including 45 subjects from healthy and chronic cough cohorts was conducted that contained various tasks including cough and cough-like body sounds in various conditions such as quiet/noisy and stationary/non-stationary. Our hybrid model was evaluated for sensitivity and specificity in these conditions using leave one-subject out validation (LOSOV) and achieved an average sensitivity of 83% for stationary tasks and an specificity of 91.7% for cough-like sounds reducing the false positive rate by 55%. These results indicate the feasibility and superiority of fusion in earbuds platforms for detection of cough events.
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Some recent works use wearables to detect cough activity, a major symptom of pulmonary diseases.
“Some recent works use wearables to detect cough activity, a major symptom of pulmonary diseases [37,38,39,40].”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [39]
Cites
Accurate and privacy preserving cough sensing using a low-cost microphone
Larson, E. C., Lee, T.-J., Liu, S. T. H., Rosenfeld, M., & Patel, S. (2011) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2030112.2030163 · Cited by 224 · see citing works
Detection of Cough Signals in Continuous Audio Recordings Using Hidden Markov Models
Matos, S., Birring, S. S., Pavord, I. D., & Evans, D. H. (2006) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2006.873548 · Cited by 216 · see citing works
Extracting Optimal Performance from Dynamic Time Warping
Mueen, A., & Keogh, E. (2016) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2939672.2945383 · Cited by 166 · see citing works
Deep Neural Networks for Identifying Cough Sounds
Amoh, J., & Odame, K. M. (2016) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2016.2598794 · Cited by 153 · see citing works
Robust Detection of Audio-Cough Events Using Local Hu Moments
Monge-Álvarez, J., Hoyos-Barceló, C., Lesso, P., & Casaseca‐de‐la‐Higuera, P. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2800741 · Cited by 96 · see citing works
Xu, X., Nemati, E., Vatanparvar, K., Nathan, V., Ahmed, T., Rahman, M. M., McCaffrey, D., Kuang, J., & Gao, J. A. (2021) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3448124 · Cited by 38 · see citing works
Self-citationPrivate Audio-Based Cough Sensing for In-Home Pulmonary Assessment Using Mobile Devices
Nemati, E., Rahman, M. M., Nathan, V., & Kuang, J. (2020) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-29897-5_18 · Cited by 25 · see citing works
Self-citationEstimation of the Lung Function Using Acoustic Features of the Voluntary Cough
Nemati, E., Rahman, M. J., Blackstock, E., Nathan, V., Rahman, M. M., Vatanparvar, K., & Kuang, J. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9175986 · Cited by 24 · see citing works
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Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
Zhang, S., Li, Y., Zhang, S., Shahabi, F., Xia, S., Deng, Y., & Alshurafa, N. (2022) · Sensors · doi:10.3390/s22041476 · Cited by 625 · see citing works
“Some recent works use wearables to detect cough activity, a major symptom of pulmonary diseases [199, 311, 329, 330] Other works have introduced methods for monitoring stroke in infants using wearable accelerometers [100] and methods for assessing depressive symptoms utilizing wrist-worn sensors [104].”
BackgroundMethodSelf-citationZhang, S., Nemati, E., Dinh, M., Folkman, N., Ahmed, T., Rahman, M., Kuang, J., Alshurafa, N., & Gao, A. (2022) · ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp43922.2022.9746334 · Cited by 23 · see citing works
Self-citation
- Authors
- Nemati, E., Zhang, S., Ahmed, T., Rahman, M. M., Kuang, J., & Gao, A.
- Venue
- 2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN)
- Publisher
- IEEE
- Published
- 2021-07-27
- Pages
- 1-4
- ISSN
- 2376-8886 (ISSN-L)
- OpenAlex
- W3195208586
- Cited by
- 20
- References
- 8
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 5 · 2023: 4 · 2022: 6
- FWCI
- 13.54
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Wearable and Implantable Body Sensor Networks (publishedVersion)
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Phonocardiography and Auscultation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Respiratory and Cough-Related Research
- Keywords
- cough detection, earbuds, sensor fusion, inertial measurement unit, head movement, template matching, chronic cough, unseen classes, multimodal sensing
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