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These embedded wearable systems are, however, limited with low available memory and computational power. Advances in machine learning, especially deep neural networks (DNNs), have been adopted for efficient and intelligent applications to overcome constrained computational environments. Herein, evolutionary algorithms are used to find novel DNNs that are accurate in classifying airway symptoms while allowing wearable deployment. As opposed to typical microphone\u2010acoustic signals, mechano\u2010acoustic data signals, which did not contain identifiable speech information for better privacy protection, are acquired from laboratory\u2010generated and publicly available datasets. The optimized DNNs had a low model file size of less than 150\u2009kB and predicted airway symptoms of interest with 81.49% accuracy on unseen data. By performing explainable AI techniques, namely occlusion experiments and class activation maps, mel\u2010frequency bands up to 8,000\u2009Hz are found as the most important feature for the classification. It is further found that DNN decisions are consistently relying on these specific features, fostering trust and transparency of the proposed DNNs. The proposed efficient and explainable DNN is expected to support edge computing on mechano\u2010acoustic sensing wearables for remote, long\u2010term monitoring of airway symptoms.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/aisy.202100284","type":"journal-article","created":{"date-parts":[[2022,5,17]],"date-time":"2022-05-17T10:56:39Z","timestamp":1652784999000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Efficient and Explainable Deep Neural Networks for Airway Symptom Detection in Support of Wearable Health Technology"],"prefix":"10.1002","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3405-1311","authenticated-orcid":false,"given":"Ren\u00e9","family":"Groh","sequence":"first","affiliation":[{"name":"Department Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg (FAU)  Werner-von-Siemens-Stra\u00dfe 61 91052 Erlangen Germany"}]},{"given":"Zhengdong","family":"Lei","sequence":"additional","affiliation":[{"name":"School of Communication Sciences &amp; Disorders McGill University  Canada 2001 McGill College 8th floor Montreal Quebec H3A 1G1 Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0228-0408","authenticated-orcid":false,"given":"Lisa","family":"Martignetti","sequence":"additional","affiliation":[{"name":"School of Communication Sciences &amp; Disorders McGill University  Canada 2001 McGill College 8th floor Montreal Quebec H3A 1G1 Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2963-4763","authenticated-orcid":false,"given":"Nicole Y. 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