{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T04:17:26Z","timestamp":1776399446023,"version":"3.51.2"},"reference-count":29,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T00:00:00Z","timestamp":1646870400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s low computational complexity and classification effectiveness translate to great potential for implementation in health-monitoring wearable devices.<\/jats:p>","DOI":"10.3390\/s22062167","type":"journal-article","created":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T20:19:10Z","timestamp":1646943550000},"page":"2167","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Distance-Based Detection of Cough, Wheeze, and Breath Sounds on Wearable Devices"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9162-098X","authenticated-orcid":false,"given":"Bing","family":"Xue","sequence":"first","affiliation":[{"name":"Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1022-6639","authenticated-orcid":false,"given":"Wen","family":"Shi","sequence":"additional","affiliation":[{"name":"Harvard Medical School, Harvard University, Cambridge, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0417-7607","authenticated-orcid":false,"given":"Sanjay H.","family":"Chotirmall","sequence":"additional","affiliation":[{"name":"Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 637551, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vivian Ci Ai","family":"Koh","sequence":"additional","affiliation":[{"name":"Aevice Health Pte. Ltd., Singapore 637551, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2592-670X","authenticated-orcid":false,"given":"Yi Yang","family":"Ang","sequence":"additional","affiliation":[{"name":"Aevice Health Pte. Ltd., Singapore 637551, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3111-7618","authenticated-orcid":false,"given":"Rex Xiao","family":"Tan","sequence":"additional","affiliation":[{"name":"Aevice Health Pte. Ltd., Singapore 637551, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wee","family":"Ser","sequence":"additional","affiliation":[{"name":"Aevice Health Pte. 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