{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:40:49Z","timestamp":1760150449205,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2023,12,6]],"date-time":"2023-12-06T00:00:00Z","timestamp":1701820800000},"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>This work addresses the design and implementation of a novel PhotoBiological Filter Classifier (PhBFC) to improve the accuracy of a static sign language translation system. The captured images are preprocessed by a contrast enhancement algorithm inspired by the capacity of retinal photoreceptor cells from mammals, which are responsible for capturing light and transforming it into electric signals that the brain can interpret as images. This sign translation system not only supports the effective communication between an agent and an operator but also between a community with hearing disabilities and other people. Additionally, this technology could be integrated into diverse devices and applications, further broadening its scope, and extending its benefits for the community in general. The bioinspired photoreceptor model is evaluated under different conditions. To validate the advantages of applying photoreceptors cells, 100 tests were conducted per letter to be recognized, on three different models (V1, V2, and V3), obtaining an average of 91.1% of accuracy on V3, compared to 63.4% obtained on V1, and an average of 55.5 Frames Per Second (FPS) in each letter classification iteration for V1, V2, and V3, demonstrating that the use of photoreceptor cells does not affect the processing time while also improving the accuracy. The great application potential of this system is underscored, as it can be employed, for example, in Deep Learning (DL) for pattern recognition or agent decision-making trained by reinforcement learning, etc.<\/jats:p>","DOI":"10.3390\/s23249646","type":"journal-article","created":{"date-parts":[[2023,12,6]],"date-time":"2023-12-06T06:01:07Z","timestamp":1701842467000},"page":"9646","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Bioinspired Photoreceptors with Neural Network for Recognition and Classification of Sign Language Gesture"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7197-8928","authenticated-orcid":false,"given":"Claudio","family":"Urrea","sequence":"first","affiliation":[{"name":"Electrical Engineering Department, Faculty of Engineering, University of Santiago of Chile, Las Sophoras 165, Estaci\u00f3n Central, Santiago 9170020, Chile"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1958-7289","authenticated-orcid":false,"given":"John","family":"Kern","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, Faculty of Engineering, University of Santiago of Chile, Las Sophoras 165, Estaci\u00f3n Central, Santiago 9170020, Chile"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5392-4002","authenticated-orcid":false,"given":"Ricardo","family":"Navarrete","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, Faculty of Engineering, University of Santiago of Chile, Las Sophoras 165, Estaci\u00f3n Central, Santiago 9170020, Chile"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,6]]},"reference":[{"key":"ref_1","unstructured":"(2023, March 01). Naciones Unidas Naciones Unidas. Available online: https:\/\/www.un.org\/es\/observances\/sign-languages-day."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zuo, R., Wei, F., and Mak, B. (2023, January 18\u201322). Natural Language-Assisted Sign Language Recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01430"},{"key":"ref_3","first-page":"1","article-title":"Two-Stream Network for Sign Language Recognition and Translation","volume":"35","author":"Chen","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Hu, L., Gao, L., Liu, Z., and Feng, W. (2023, January 18\u201322). Continuous Sign Language Recognition with Correlation Network. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00249"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1880","DOI":"10.1109\/TMM.2018.2889563","article-title":"A Deep Neural Framework for Continuous Sign Language Recognition by Iterative Training","volume":"21","author":"Cui","year":"2019","journal-title":"IEEE Trans. Multimed."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Shihabuddin, A.R., and Beevi, S. (2023). Multi CNN Based Automatic Detection of Mitotic Nuclei in Breast Histopathological Images. Comput. Biol. Med., 158.","DOI":"10.1016\/j.compbiomed.2023.106815"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Krishan, A., Ritam, M., Han, L., Guo, S., and Chandra, R. (2022). Deep Learning for Predicting Respiratory Rate from Biosignals. Comput. Biol. Med., 144.","DOI":"10.1016\/j.compbiomed.2022.105338"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gon\u00e7alves, C.B., Souza, J.R., and Fernandes, H. (2022). CNN Architecture Optimization Using Bio-Inspired Algorithms for Breast Cancer Detection in Infrared Images. Comput. Biol. Med., 142.","DOI":"10.1016\/j.compbiomed.2021.105205"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Isaac, A., Nehemiah, H.K., Isaac, A., and Kannan, A. (2020). Computer-Aided Diagnosis System for Diagnosis of Pulmonary Emphysema Using Bio-Inspired Algorithms. Comput. Biol. Med., 124.","DOI":"10.1016\/j.compbiomed.2020.103940"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"108762","DOI":"10.1016\/j.patcog.2022.108762","article-title":"3D Hand Pose and Shape Estimation from RGB Images for Keypoint-Based Hand Gesture Recognition","volume":"129","author":"Avola","year":"2022","journal-title":"Pattern Recongit."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.patcog.2019.05.015","article-title":"Bio-Inspired Digit Recognition Using Reward-Modulated Spike-Timing-Dependent Plasticity in Deep Convolutional Networks","volume":"94","author":"Mozafari","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.patcog.2017.10.033","article-title":"Convolutional Neural Networks and Long Short-Term Memory for Skeleton-Based Human Activity and Hand Gesture Recognition","volume":"76","author":"Cabido","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"108200","DOI":"10.1016\/j.patcog.2021.108200","article-title":"Unified Learning Approach for Egocentric Hand Gesture Recognition and Fingertip Detection","volume":"121","author":"Mahmudul","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lehnert, H., and Mar, S. (2019, January 14\u201319). Retina-Inspired Visual Module for Robot Navigation in Complex Environments. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary.","DOI":"10.1109\/IJCNN.2019.8851896"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhao, J., Hu, C., Zhang, C., Wang, Z., and Yue, S. (2018, January 8\u201313). A Bio-Inspired Collision Detector for Small Quadcopter. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489298"},{"key":"ref_16","unstructured":"Nagaraj, A. (2023, July 19). ASL Alphabet Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/grassknoted\/asl-alphabet."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Xia, K., Lu, W., Fan, H., and Zhao, Q. (2022). A Sign Language Recognition System Applied to Deaf-Mute Medical Consultation. Sensors, 22.","DOI":"10.3390\/s22239107"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kim, Y., and Hyeongboo, B. (2023). Preprocessing for Keypoint-Based Sign Language Translation without Glosses. Sensors, 23.","DOI":"10.3390\/s23063231"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Korzeniewska, E., Kania, M., and Zawi, R. (2022). Textronic Glove Translating Polish Sign Language. Sensors, 22.","DOI":"10.3390\/s22186788"},{"key":"ref_20","unstructured":"(2020, December 20). BBC Aplicaci\u00f3n Para Lenguaje de Se\u00f1as En Smartphone. Available online: https:\/\/www.bbc.com\/mundo\/noticias\/2012\/03\/120312_lenguaje_senas_texto_app_adz."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"26319","DOI":"10.1007\/s11042-021-10768-5","article-title":"ASL-3DCNN: American Sign Language Recognition Technique Using 3-D Convolutional Neural Networks","volume":"80","author":"Sharma","year":"2021","journal-title":"Multimedia Tools Appl."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chen, X., Su, L., Zhao, J., Qiu, K., and Jiang, N. (2023). Sign Language Gesture Recognition and Classification Based on Event Camera with Spiking Neural Networks. Electronics, 12.","DOI":"10.3390\/electronics12040786"},{"key":"ref_23","unstructured":"Arjun, A.M., and Sreehari, S.N.R. (2020, January 11\u201313). The Interplay Of Hand Gestures And Facial Expressions In Conveying Emotions. Proceedings of the 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), Erode, India."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Harini, R., Janani, R., Keerthana, S., and Madhubala, S.S.V. (2020, January 6\u20137). Sign Language Traslation. Proceedings of the 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India.","DOI":"10.1109\/ICACCS48705.2020.9074370"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"821","DOI":"10.18178\/ijmlc.2019.9.6.879","article-title":"Static Sign Language Recognition Using Deep Learning","volume":"9","author":"Tolentino","year":"2019","journal-title":"Int. J. Mach. Learn. Comput."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"139946","DOI":"10.1109\/ACCESS.2021.3118829","article-title":"A New Approach for Video Action Recognition: CSP-Based Filtering for Video to Image Transformation","volume":"9","author":"Goienetxea","year":"2021","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1384","DOI":"10.1016\/j.procs.2023.01.117","article-title":"Real-Time Assamese Sign Language Recognition Using MediaPipe and Deep Learning","volume":"218","author":"Bora","year":"2023","journal-title":"Procedia Comput. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"104358","DOI":"10.1109\/ACCESS.2022.3210543","article-title":"Development of an End-to-End Deep Learning Framework for Sign Language Recognition, Translation, and Video Generation","volume":"10","author":"Natarajan","year":"2022","journal-title":"IEEE Access"},{"key":"ref_29","first-page":"5327","article-title":"Intelligent Sign Language Recognition System for E-Learning Context","volume":"72","author":"Hussain","year":"2022","journal-title":"Comput. Mater. Contin."},{"key":"ref_30","first-page":"421","article-title":"Sign Language Recognition Using Deep Learning","volume":"13","author":"Ray","year":"2022","journal-title":"J. Pharm. Negat. Results"},{"key":"ref_31","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_32","doi-asserted-by":"crossref","unstructured":"G\u00fcney, G., Jansen, T.S., Dill, S., Schulz, J.B., Dafotakis, M., Hoog Antink, C., and Braczynski, A.K. (2022). Video-Based Hand Movement Analysis of Parkinson Patients before and after Medication Using High-Frame-Rate Videos and MediaPipe. Sensors, 22.","DOI":"10.3390\/s22207992"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Jeong, S.O., and Kook, J. (2023). CREBAS: Computer-Based REBA Evaluation System for Wood Manufacturers Using MediaPipe. Appl. Sci., 13.","DOI":"10.3390\/app13020938"},{"key":"ref_34","unstructured":"Escobar, M.-J. (2009). Bio-Inspired Models for Motion Estimation and Analysis: Human Action Recognition and Motion Integration. [Ph.D. Thesis, Universit\u00e9 de Nice Sophia-Antipolis]."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5580","DOI":"10.1109\/TIP.2019.2919947","article-title":"Underwater Image Enhancement Using Adaptive Retinal Mechanisms","volume":"28","author":"Li","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ocko, S.A., Lindsey, J., Ganguli, S., and Deny, S. (2018). The Emergence of Multiple Retinal Cell Types through Efficient Coding of Natural Movies. bioRxiv, 31.","DOI":"10.1101\/458737"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3484","DOI":"10.1109\/TIP.2018.2812079","article-title":"Retina-Inspired Filter","volume":"27","author":"Doutsi","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1016\/j.cviu.2010.01.011","article-title":"Using Human Visual System Modeling for Bio-Inspired Low Level Image Processing","volume":"114","author":"Benoit","year":"2010","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_39","unstructured":"Herault, J. (2007). Computational and Ambient Intelligence, Proceedings of the9th International Work-Conference on Artificial Neural Networks, IWANN 2007, San Sebasti\u00e1n, Spain, 20\u201322 June 2007, Springer."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/24\/9646\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:33:54Z","timestamp":1760132034000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/24\/9646"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,6]]},"references-count":39,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["s23249646"],"URL":"https:\/\/doi.org\/10.3390\/s23249646","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,12,6]]}}}