A Novel Multi-Centroid Template Matching Algorithm and Its Application to Cough Detection
Open accessAbstract
Cough is a major symptom of respiratory-related diseases. There exists a tremendous amount of work in detecting coughs from audio but there has been no effort to identify coughs from solely inertial measurement unit (IMU). Coughing causes motion across the whole body and especially on the neck and head. Therefore, head motion data during coughing captured by a head-worn IMU sensor could be leveraged to detect coughs using a template matching algorithm. In time series template matching problems, K-Nearest Neighbors (KNN) combined with elastic distance measurement (esp. Dynamic Time Warping (DTW)) achieves outstanding performance. However, it is often regarded as prohibitively time-consuming. Nearest Centroid Classifier is thereafter proposed. But the accuracy is comprised of only one centroid obtained for each class. Centroid-based Classifier performs clustering and averaging for each cluster, but requires manually setting the number of clusters. We propose a novel self-tuning multi-centroid template-matching algorithm, which can automatically adjust the number of clusters to balance accuracy and inference time. Through experiments conducted on synthetic datasets and a real-world earbud-based cough dataset, we demonstrate the superiority of our proposed algorithm and present the result of cough detection with a single accelerometer sensor on the earbuds platform.Clinical relevance- Coughing is a ubiquitous symptom of pulmonary disease, especially for patients with COPD and asthma. This work explores the possibility and and presents the result of cough detection using an IMU sensor embedded in earables.
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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 [38]
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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].”
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Self-citation
- Authors
- Zhang, S., Nemati, E., Ahmed, T., Rahman, M. M., Kuang, J., & Gao, A.
- Affiliations
- Northwestern University (US)
- Venue
- 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
- Publisher
- IEEE
- Published
- 2021-11-01
- Volume
- 2021
- Pages
- 7598-7604
- ISSN
- 2375-7477 (ISSN-L)
- OpenAlex
- W3197225657
- Cited by
- 5
- References
- 20
- Citations by year
- 2023: 2 · 2022: 3
- FWCI
- 0.76
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Samsung
- Topics
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- cough detection, inertial measurement unit, earbuds, template matching, dynamic time warping, accelerometer, respiratory disease monitoring
- MeSH terms
- Algorithms, Asthma, Cough, Humans, Time Factors, Cluster Analysis
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