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Distance-Based Detection of Cough, Wheeze, and Breath Sounds on Wearable Devices
- Source :
- Sensors, Vol 22, Iss 6, p 2167 (2022)
- Publication Year :
- 2022
- Publisher :
- MDPI AG, 2022.
-
Abstract
- Smart wearable sensors are essential for continuous health-monitoring applications and detection accuracy of symptoms and energy efficiency of processing algorithms are key challenges for such devices. While several machine-learning-based algorithms for the detection of abnormal breath sounds are reported in literature, they are either too computationally expensive to implement into a wearable device or inaccurate in multi-class detection. In this paper, a kernel-like minimum distance classifier (K-MDC) for acoustic signal processing in wearable devices was proposed. The proposed algorithm was tested with data acquired from open-source databases, participants, and hospitals. It was observed that the proposed K-MDC classifier achieves accurate detection in up to 91.23% of cases, and it reaches various detection accuracies with a fewer number of features compared with other classifiers. The proposed algorithm’s low computational complexity and classification effectiveness translate to great potential for implementation in health-monitoring wearable devices.
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 22
- Issue :
- 6
- Database :
- Directory of Open Access Journals
- Journal :
- Sensors
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.7bf2fce78a02453cb895e5079d87b24c
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/s22062167