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The integration of this neuromorphic hardware with AlexNet deep-learning algorithm enables the efficient extraction of human respiratory patterns, achieving 95.56% accuracy in identifying individual \u201cbreathing fingerprints.\u201d Our work underscores the potential of bioinspired neuromorphic systems to bridge the gap between biological perception and artificial sensing, establishing a neuromorphic front-end design paradigm that advances next-generation brain-inspired computing.\n                  <\/jats:p>","DOI":"10.1073\/pnas.2617376123","type":"journal-article","created":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T17:54:21Z","timestamp":1787248461000},"update-policy":"https:\/\/doi.org\/10.1073\/pnas.cm10313","source":"Crossref","is-referenced-by-count":0,"title":["A hypersensitive neuromorphic airflow sensor inspired by vision-compensatory scorpion mechanoreceptors for respiratory pattern 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