{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T21:49:11Z","timestamp":1791928151155,"version":"4.3.4"},"reference-count":35,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T00:00:00Z","timestamp":1762560000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"crossref","award":["PNRR-PRIN 2022 P20223R3R4"],"award-info":[{"award-number":["PNRR-PRIN 2022 P20223R3R4"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The present study evaluates the effectiveness of a non-invasive wearable sensor system, combining accelerometers, surface electromyography, and artificial intelligence, to objectively characterize swallowing in elderly individuals affected by Parkinson\u2019s Disease, without clinically manifested dysphagia. A cohort of patients and healthy control subjects performed the same swallowing test protocol, including tasks with different viscosity boluses, positioning a commercial adhesive grid of High-Density surface Electromyography (HD-sEMG) electrodes on the submental muscle and a triaxial accelerometer over the thyroid cartilage. Relevant temporal and spectral features were extracted from electromyography data. Proper filtering and processing by machine learning and Principal Component Analysis allowed identification of two distinct clusters of subjects, one predominantly composed of controls with just a few patients, the other mostly crowded by patients. Excellent classification performances were achieved (accuracy = 83.3%, precision = 79.0%, recall = 90.7%, F1-score = 84.5%, Cohen\u2019s kappa = 0.67), revealing consistent differences in muscle activation patterns among subjects, even in the absence of clinically diagnosed dysphagia. These results support the feasibility of wearable sensor-based assessment as a reliable and non-invasive tool for the early detection of subclinical swallowing dysfunction in Parkinson\u2019s Disease.<\/jats:p>","DOI":"10.3390\/s25226834","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T15:07:31Z","timestamp":1762787251000},"page":"6834","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Early Detection of Dysphagia Signs in Parkinson\u2019s Disease: An Artificial Intelligence-Based Approach Using Non-Invasive Sensors"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-0683-7971","authenticated-orcid":false,"given":"Michele Antonio","family":"Gazzanti Pugliese di Cotrone","sequence":"first","affiliation":[{"name":"Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, 00185 Rome, Italy"},{"name":"Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nid\u00e0 Farooq","family":"Akhtar","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, 00185 Rome, Italy"},{"name":"Department of Electronic Engineering, Universitat Polit\u00e8cnica de Catalunya, 08034 Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8677-0468","authenticated-orcid":false,"given":"Martina","family":"Patera","sequence":"additional","affiliation":[{"name":"Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, 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