{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T01:26:34Z","timestamp":1774401994175,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T00:00:00Z","timestamp":1770336000000},"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":["101140156"],"award-info":[{"award-number":["101140156"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Deafness poses significant challenges to effective communication, particularly in contexts where access to sign language interpreters is limited. Hand configuration recognition represents a fundamental component of sign language understanding, as configurations constitute a core cheremic element in many sign languages, including Italian Sign Language (LIS). In this work, we address configuration-level recognition as an independent classification task and propose a machine vision framework based on RGB-D sensing. The proposed approach combines MediaPipe-based hand landmark extraction with normalized three-dimensional geometric features and a Support Vector Machine classifier. The first contribution of this study is the formulation of LIS hand configuration recognition as a standalone, configuration-level problem, decoupled from temporal gesture modeling. The second contribution is the integration of sensor-acquired RGB-D depth measurements into the landmark-based feature representation, enabling a direct comparison with estimated depth obtained from monocular data. The third contribution consists of a systematic experimental evaluation on two LIS configuration sets (6 and 16 classes), demonstrating that the use of real depth significantly improves classification performance and class separability, particularly for geometrically similar configurations. The results highlight the critical role of depth quality in configuration-level recognition and provide insights into the design of robust vision-based systems for LIS analysis.<\/jats:p>","DOI":"10.3390\/s26031059","type":"journal-article","created":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T10:05:47Z","timestamp":1770372347000},"page":"1059","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A 3D Camera-Based Approach for Real-Time Hand Configuration Recognition in Italian Sign Language"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8407-0660","authenticated-orcid":false,"given":"Luca","family":"Ulrich","sequence":"first","affiliation":[{"name":"Management and Production Engineering, Politecnico di Torino, C.so Duca degli Abruzzi, 24, 10129 Torino, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Asia","family":"De Luca","sequence":"additional","affiliation":[{"name":"Biomedical Engineering, Politecnico di Torino, C.so Duca degli Abruzzi, 24, 10129 Torino, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Riccardo","family":"Miraglia","sequence":"additional","affiliation":[{"name":"Biomedical Engineering, Politecnico di Torino, C.so Duca degli Abruzzi, 24, 10129 Torino, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emma","family":"Mulassano","sequence":"additional","affiliation":[{"name":"Biomedical Engineering, Politecnico di Torino, C.so Duca degli Abruzzi, 24, 10129 Torino, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simone","family":"Quattrocchio","sequence":"additional","affiliation":[{"name":"Biomedical Engineering, Politecnico di Torino, C.so Duca degli Abruzzi, 24, 10129 Torino, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2359-4409","authenticated-orcid":false,"given":"Giorgia","family":"Marullo","sequence":"additional","affiliation":[{"name":"Management and Production Engineering, Politecnico di Torino, C.so Duca degli Abruzzi, 24, 10129 Torino, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7839-6218","authenticated-orcid":false,"given":"Chiara","family":"Innocente","sequence":"additional","affiliation":[{"name":"Management and Production Engineering, Politecnico di Torino, C.so Duca degli Abruzzi, 24, 10129 Torino, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5282-9513","authenticated-orcid":false,"given":"Federico","family":"Salerno","sequence":"additional","affiliation":[{"name":"Management and Production Engineering, Politecnico di Torino, C.so Duca degli Abruzzi, 24, 10129 Torino, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8910-7020","authenticated-orcid":false,"given":"Enrico","family":"Vezzetti","sequence":"additional","affiliation":[{"name":"Management and Production Engineering, Politecnico di Torino, C.so Duca degli Abruzzi, 24, 10129 Torino, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"La Grutta, S., Piombo, M.A., Spicuzza, V., Riolo, M., Fanara, I., Trombini, E., Andrei, F., and Epifanio, M.S. (2023). The Relationship between Knowing Sign Language and Quality of Life among Italian People Who Are Deaf: A Cross-Sectional Study. Healthcare, 11.","DOI":"10.3390\/healthcare11071021"},{"key":"ref_2","first-page":"10","article-title":"Health Care Access Among Deaf People","volume":"1","author":"Kuenburg","year":"2016","journal-title":"J. Deaf Stud. Deaf Educ."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"James, T.G., Varnes, J.R., Sullivan, M.K., Cheong, J., Pearson, T.A., Yurasek, A.M., Miller, M.D., and McKee, M.M. (2021). Conceptual Model of Emergency Department Utilization among Deaf and Hard-of-Hearing Patients: A Critical Review. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph182412901"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1016\/j.dhjo.2018.07.010","article-title":"State of research and program development for adults with hearing loss","volume":"11","author":"McKee","year":"2018","journal-title":"Disabil. Health J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1016\/j.annemergmed.2018.05.009","article-title":"Use of Interpreter Services in the Emergency Department","volume":"72","author":"Brenner","year":"2018","journal-title":"Ann. Emerg. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e883","DOI":"10.7717\/peerj-cs.883","article-title":"Recognition of Urdu sign language: A systematic review of the machine learning classification","volume":"8","author":"Zahid","year":"2022","journal-title":"PeerJ Comput. Sci."},{"key":"ref_7","unstructured":"Koller, O. (2020). Quantitative Survey of the State of the Art in Sign Language Recognition. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ulrich, L., Carmassi, G., Garelli, P., Lo Presti, G., Ramondetti, G., Marullo, G., Innocente, C., and Vezzetti, E. (2024). SIGNIFY: Leveraging machine learning and gesture recognition for sign language teaching through a serious game. Future Internet, 16.","DOI":"10.3390\/fi16120447"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Rasetto, S., Marullo, G., Adamo, L., Bordin, F., Pavesi, F., Innocente, C., Vezzetti, E., and Ulrich, L. (2025). ReHAb Playground: A DL-Based Framework for Game-Based Hand Rehabilitation. Future Internet, 17.","DOI":"10.3390\/fi17110522"},{"key":"ref_10","first-page":"138","article-title":"LIS e semiotica","volume":"1","author":"Mazzaraco","year":"2010","journal-title":"Studi Glottodidattica"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"de Souza, C.R., and Pizzolato, E.B. (2013). Sign Language Recognition with Support Vector Machines and Hidden Conditional Random Fields: Going from Fingerspelling to Natural Articulated Words. Machine Learning and Data Mining in Pattern Recognition, Springer.","DOI":"10.1007\/978-3-642-39712-7_7"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"100141","DOI":"10.1016\/j.array.2022.100141","article-title":"Indian Sign Language recognition system using SURF with SVM and CNN","volume":"14","author":"Katoch","year":"2022","journal-title":"Array"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Podder, K.K., Ezeddin, M., Chowdhury, M.E.H., Sumon, M.S.I., Tahir, A.M., Ayari, M.A., Dutta, P., Khandakar, A., Mahbub, Z.B., and Kadir, M.A. (2023). Signer-Independent Arabic Sign Language Recognition System Using Deep Learning Model. Sensors, 23.","DOI":"10.3390\/s23167156"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s44163-024-00113-8","article-title":"Using LSTM to translate Thai sign language to text in real time","volume":"4","author":"Jintanachaiwat","year":"2024","journal-title":"Discov. Artif. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Oudah, M., Al-Naji, A., and Chahl, J. (2020). Hand Gesture Recognition Based on Computer Vision: A Review of Techniques. J. Imaging, 6.","DOI":"10.3390\/jimaging6080073"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5809769","DOI":"10.1155\/2018\/5809769","article-title":"Kinect Sensor-Based Long-Distance Hand Gesture Recognition and Fingertip Detection with Depth Information","volume":"2018","author":"Ma","year":"2018","journal-title":"J. Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-Moreno, I., Mart\u00ednez-Otzeta, J.M., Goienetxea, I., and Sierra, B. (2022). Sign language recognition by means of common spatial patterns: An analysis. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0276941"},{"key":"ref_18","first-page":"561","article-title":"Recognition of static hand gesture with using ANN and SVM","volume":"10","author":"Bamwend","year":"2019","journal-title":"Dicle Univ. J. Eng."},{"key":"ref_19","unstructured":"de Smedt, Q., Wannous, H., Vandeborre, J.P., Guerry, J., Le Saux, B., and Filliat, D. (2017, January 23\u201324). SHREC\u201917 Track: 3D Hand Gesture Recognition Using a Depth and Skeletal Dataset. Proceedings of the 3DOR-10th Eurographics Workshop on 3D Object Retrieval, Lyon, France."},{"key":"ref_20","first-page":"415","article-title":"Dynamic Gesture Classification for Vietnamese Sign Language Recognition","volume":"8","author":"Vo","year":"2017","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kakizaki, M., Miah, A.S.M., Hirooka, K., and Shin, J. (2024). Dynamic Japanese Sign Language Recognition Through Hand Pose Estimation Using Effective Feature Extraction and Classification Approach. Sensors, 24.","DOI":"10.3390\/s24030826"},{"key":"ref_22","first-page":"364","article-title":"Real-Time Static Hand Gesture Recognition for American Sign Language (ASL) in Complex Background","volume":"3","author":"Pansare","year":"2012","journal-title":"J. Signal Inf. Process."},{"key":"ref_23","first-page":"3986497","article-title":"Hand Gesture Recognition Algorithm Using SVM and HOG Model for Control of Robotic System","volume":"2021","year":"2021","journal-title":"J. Robot."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1145\/1897816.1897838","article-title":"Vision-Based Hand-Gesture Applications","volume":"54","author":"Wachs","year":"2011","journal-title":"Commun. ACM"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Saini, T., and Kumari, N. (2024, January 14\u201315). SignaSpectrum: AI-Driven Dynamic Sign Language Detection and Interpretation. Proceedings of the 11th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO), Noida, India.","DOI":"10.1109\/ICRITO61523.2024.10522423"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Amangeldy, N., Kudubayeva, S., Kassymova, A., Karipzhanova, A., Razakhova, B., and Kuralov, S. (2022). Sign Language Recognition Method Based on Palm Definition Model and Multiple Classification. Sensors, 22.","DOI":"10.3390\/s22176621"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4567989","DOI":"10.1155\/2022\/4567989","article-title":"Sign Language Recognition for Arabic Alphabets Using Transfer Learning Technique","volume":"2022","author":"Zakariah","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.procs.2018.07.008","article-title":"Recognition of Sign Language Alphabets and Numbers based on Hand Kinematics using A Data Glove","volume":"133","author":"Kakoty","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"7259","DOI":"10.1016\/j.eswa.2014.05.024","article-title":"Feature extraction in Brazilian Sign Language Recognition based on phonological structure and using RGB-D sensors","volume":"41","author":"Almeida","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Saravanan, R., and Veluchamy, S. (2023, January 14\u201315). Sign Language Classification with MediaPipe Hand Landmarks. Proceedings of the International Conference on Energy, Materials and Communication Engineering (ICEMCE), Madurai, India.","DOI":"10.1109\/ICEMCE57940.2023.10434034"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ameur, S., and Bouhlel, M.S. (2016, January 18\u201320). A Comprehensive Leap Motion Database for Hand Gesture Recognition. Proceedings of the 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), Hammamet, Tunisia.","DOI":"10.1109\/SETIT.2016.7939924"},{"key":"ref_32","first-page":"2456","article-title":"Real-time Myanmar Sign Language Recognition System using PCA and SVM","volume":"3","author":"Tun","year":"2019","journal-title":"Int. J. Trend Sci. Res. Dev. (IJTSRD)"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Xue, Q., Li, X., Wang, D., and Zhang, W. (2019). Deep Forest-Based Monocular Visual Sign Language Recognition. Appl. Sci., 9.","DOI":"10.3390\/app9091945"},{"key":"ref_34","unstructured":"Ariesta, M.C., Wiryana, F., and Zahra, A. (2018, January 7\u20138). Sentence Level Indonesian Sign Language Recognition Using 3D CNN and BiRNN. Proceedings of the 1st INAPR International Conference, Jakarta, Indonesia."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1353\/sls.2002.0012","article-title":"Iconicity and arbitrariness in Italian sign language","volume":"2","author":"Pietrandrea","year":"2002","journal-title":"Sign Lang. Stud."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2333","DOI":"10.1109\/TPAMI.2019.2915841","article-title":"Intel\u00ae realsenseTM sr300 coded light depth camera","volume":"42","author":"Zabatani","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_37","unstructured":"Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.L., Yong, M.G., and Lee, J. (2019). MediaPipe: A Framework for Building Perception Pipelines. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"102","DOI":"10.4236\/jcc.2023.112008","article-title":"Detection of 3D Human Posture Based on Improved Mediapipe","volume":"11","author":"Lin","year":"2023","journal-title":"J. Comput. Commun."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"111643","DOI":"10.1016\/j.measurement.2022.111643","article-title":"A methodology for task-specific metrological characterization of low-cost 3D camera for face analysis","volume":"200","author":"Maculotti","year":"2022","journal-title":"Measurement"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.patrec.2020.07.042","article-title":"Comparative analysis of image classification algorithms based on traditional machine learning and deep learning","volume":"141","author":"Wang","year":"2021","journal-title":"Pattern Recognit. Lett."},{"key":"ref_41","first-page":"1485","article-title":"Robustness and regularization of support vector machines","volume":"10","author":"Xu","year":"2009","journal-title":"J. Mach. Learn. Res."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Dinh, N.S., Nguyen, T.D., Tran, D.T., Pham, N.D.H., Tran, T.H., Tong, N.A., Hoang, Q.H., and Le Nguyen, P. (2025). Sign Language Recognition: A Large-scale Multi-view Dataset and Comprehensive Evaluation. 2025 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), IEEE.","DOI":"10.1109\/WACV61041.2025.00766"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/3\/1059\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T05:16:35Z","timestamp":1770527795000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/3\/1059"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,6]]},"references-count":42,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["s26031059"],"URL":"https:\/\/doi.org\/10.3390\/s26031059","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,6]]}}}