{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T08:40:51Z","timestamp":1769848851025,"version":"3.49.0"},"reference-count":87,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T00:00:00Z","timestamp":1743724800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University of Rijeka","award":["uniri-iskusni-tehnic-23-120"],"award-info":[{"award-number":["uniri-iskusni-tehnic-23-120"]}]},{"name":"University of Rijeka","award":["uniri-mladi-tehnic-23-41"],"award-info":[{"award-number":["uniri-mladi-tehnic-23-41"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper deals with biometric identification based on unique patterns and characteristics of an individual\u2019s handwriting, focusing on the dynamic writing process on a touchscreen device. Related work in this domain indicates the dominance of specific research approaches. Namely, in most cases, only the signature is analyzed, verification methods are more prevalent than recognition methods, and the provided solutions are mainly based on using a particular device or specific sensor for collecting biometric data. In this context, our work aims to fill the identified research gap by introducing a new handwriting-based user recognition technique. The proposed approach implements the concept of sensor fusion and does not rely exclusively on signatures for recognition but also includes other forms of handwriting, such as short sentences, words, or individual letters. Additionally, two different ways of handwriting input, using a stylus and a finger, are introduced into the analysis. In order to collect data on the dynamics of handwriting and signing, a specially designed apparatus was used with various sensors integrated into common smart devices, along with additional external sensors and accessories. A total of 60 participants took part in a controlled experiment to form a handwriting biometrics dataset for further analysis. To classify participants\u2019 handwriting, custom architecture CNN models were utilized for feature extraction and classification tasks. The obtained results showed that the proposed handwriting recognition system achieves accuracies of 0.982, 0.927, 0.884, and 0.661 for signatures, words, short sentences, and individual letters, respectively. We further investigated the main effects of the input modality and the train set\u2019s size on the system\u2019s accuracy. Finally, an ablation study was carried out to analyze the impact of individual sensors within the fusion-based setup.<\/jats:p>","DOI":"10.3390\/s25072290","type":"journal-article","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T03:36:45Z","timestamp":1743737805000},"page":"2290","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Beyond Signatures: Leveraging Sensor Fusion for Contextual Handwriting Recognition"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4892-817X","authenticated-orcid":false,"given":"Alen","family":"Salkanovic","sequence":"first","affiliation":[{"name":"University of Rijeka, Faculty of Engineering, Vukovarska 58, HR-51000 Rijeka, Croatia"},{"name":"Center for Artificial Intelligence and Cybersecurity, University of Rijeka, R. Matejcic 2, HR-51000 Rijeka, Croatia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1026-7345","authenticated-orcid":false,"given":"Diego","family":"Su\u0161anj","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Juraj Dobrila University of Pula, Alga Negrija 6, HR-52100 Pula, Croatia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5430-6724","authenticated-orcid":false,"given":"Luka","family":"Batisti\u0107","sequence":"additional","affiliation":[{"name":"University of Rijeka, Faculty of Engineering, Vukovarska 58, HR-51000 Rijeka, Croatia"},{"name":"Center for Artificial Intelligence and Cybersecurity, University of Rijeka, R. Matejcic 2, HR-51000 Rijeka, Croatia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3456-8369","authenticated-orcid":false,"given":"Sandi","family":"Ljubic","sequence":"additional","affiliation":[{"name":"University of Rijeka, Faculty of Engineering, Vukovarska 58, HR-51000 Rijeka, Croatia"},{"name":"Center for Artificial Intelligence and Cybersecurity, University of Rijeka, R. Matejcic 2, HR-51000 Rijeka, Croatia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"64300","DOI":"10.1109\/ACCESS.2024.3395417","article-title":"A Comprehensive Review of Unimodal and Multimodal Fingerprint Biometric Authentication Systems: Fusion, Attacks, and Template Protection","volume":"12","author":"Sumalatha","year":"2024","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1007\/s12652-021-03356-w","article-title":"Signature identification and verification techniques: State-of-the-art work","volume":"14","author":"Kaur","year":"2021","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Radhika, K.R., Sekhar, G.N., and Venkatesha, M. (2009, January 2\u20134). 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