CoughBuddy: Multi-Modal Cough Event Detection Using Earbuds Platform
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...
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| Published in: | Proceedings (International Conference on Wearable and Implantable Body Sensor Networks : Print) pp. 1 - 4 |
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| Main Authors: | , , , , , |
| Format: | Conference Proceeding |
| Language: | English |
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IEEE
27.07.2021
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| ISSN: | 2376-8894 |
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| 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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| AbstractList | 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. |
| Author | Zhang, Shibo Gao, Alex Kuang, Jilong Ahmed, Tousif Nemati, Ebrahim Rahman, Md. Mahbubur |
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| Snippet | There has been an extensive amount of study on cough detection using acoustic features captured from smartphones and smartwatches in the past decade. However,... |
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| SubjectTerms | Conferences DTW Event detection Feature extraction Measurement units Sensitivity Sensitivity and specificity Sensor fusion template matching |
| Title | CoughBuddy: Multi-Modal Cough Event Detection Using Earbuds Platform |
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