{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T17:09:51Z","timestamp":1790442591848,"version":"4.1.0"},"reference-count":37,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T00:00:00Z","timestamp":1766102400000},"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>In Mexico, the Deaf community faces persistent communication barriers that restrict their integration and access to essential services, particularly in healthcare. Even though approximately two million individuals use Mexican Sign Language (MSL) as their primary form of communication, technological tools for supporting effective interaction remain limited. While recent research in sign-language recognition has led to important advances for several languages, work focused on MSL, particularly for healthcare scenarios, remains scarce. To address this gap, this study introduces a health-oriented dataset of 150 signs, with 800 synthetic video sequences per word, totaling more than 35 GB of data. This dataset was used to train recurrent neural networks with regularization and data augmentation. The best configuration achieved a maximum precision of 98.36% in isolated sign classification, minimizing false positives, which is an essential requirement in clinical applications. Beyond isolated recognition, the main contribution of this study is its exploratory evaluation of sequential narrative inference in MSL. Using short scripted narratives, the system achieved a global sequential recall of 45.45% under a realistic evaluation protocol that enforces temporal alignment. These results highlight both the potential of recurrent architectures in generalizing from isolated gestures to structured sequences and the substantial challenges posed by continuous signing, co-articulation, and signer-specific variation. While not intended for clinical deployment, the methodology, dataset, and open-source implementation presented here establish a reproducible baseline for future research. This work provides initial evidence, tools, and insights to support the long-term development of accessible technologies for the Deaf community in Mexico.<\/jats:p>","DOI":"10.3390\/s26010027","type":"journal-article","created":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T08:35:27Z","timestamp":1766392527000},"page":"27","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Recurrent Neural Networks for Mexican Sign Language Interpretation in Healthcare Services"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4420-1442","authenticated-orcid":false,"given":"Armando de Jes\u00fas","family":"Becerril-Carrillo","sequence":"first","affiliation":[{"name":"Faculty of Engineering, Universidad An\u00e1huac M\u00e9xico, Avenida Universidad An\u00e1huac 46, Col. Lomas An\u00e1huac, Huixquilucan 52786, Estado de Mexico, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0836-4733","authenticated-orcid":false,"given":"H\u00e9ctor Juli\u00e1n","family":"Selley-Rojas","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Universidad An\u00e1huac M\u00e9xico, Avenida Universidad An\u00e1huac 46, Col. Lomas An\u00e1huac, Huixquilucan 52786, Estado de Mexico, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2911-1957","authenticated-orcid":false,"given":"Elizabeth","family":"Guevara-Mart\u00ednez","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Universidad An\u00e1huac M\u00e9xico, Avenida Universidad An\u00e1huac 46, Col. Lomas An\u00e1huac, Huixquilucan 52786, Estado de Mexico, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,19]]},"reference":[{"key":"ref_1","unstructured":"Secretar\u00eda de Salud (2025, November 27). Con Discapacidad Auditiva, 2.3 Millones de Personas: Instituto Nacional de Rehabilitaci\u00f3n (2.3 Million People with Hearing Disabilities in Mexico, Reports the National Institute of Rehabilitation), Available online: https:\/\/www.gob.mx\/salud\/prensa\/530-con-discapacidad-auditiva-2-3-millones-de-personas-instituto-nacional-de-rehabilitacion?idiom=es."},{"key":"ref_2","unstructured":"World Federation of the Deaf (2025, November 27). WFD Global Report 2019\u20132023. Available online: https:\/\/wfdeaf.org\/wp-content\/uploads\/2019-2023-WFD-Report.pdf."},{"key":"ref_3","unstructured":"Comisi\u00f3n Econ\u00f3mica para Am\u00e9rica Latina y el Caribe (CEPAL) (2021). 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