{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T23:08:11Z","timestamp":1769382491936,"version":"3.49.0"},"reference-count":47,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T00:00:00Z","timestamp":1721088000000},"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>The aim of this study is to develop a practical software solution for real-time recognition of sign language words using two arms. This will facilitate communication between hearing-impaired individuals and those who can hear. We are aware of several sign language recognition systems developed using different technologies, including cameras, armbands, and gloves. However, the system we propose in this study stands out for its practicality, utilizing surface electromyography (muscle activity) and inertial measurement unit (motion dynamics) data from both arms. We address the drawbacks of other methods, such as high costs, low accuracy due to ambient light and obstacles, and complex hardware requirements, which have limited their practical application. Our software can run on different operating systems using digital signal processing and machine learning methods specific to this study. For the test, we created a dataset of 80 words based on their frequency of use in daily life and performed a thorough feature extraction process. We tested the recognition performance using various classifiers and parameters and compared the results. The random forest algorithm emerged as the most successful, achieving a remarkable 99.875% accuracy, while the na\u00efve Bayes algorithm had the lowest success rate with 87.625% accuracy. The new system promises to significantly improve communication for people with hearing disabilities and ensures seamless integration into daily life without compromising user comfort or lifestyle quality.<\/jats:p>","DOI":"10.3390\/s24144613","type":"journal-article","created":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T15:15:19Z","timestamp":1721229319000},"page":"4613","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Novel Wearable System to Recognize Sign Language in Real Time"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5269-1128","authenticated-orcid":false,"given":"\u0130lhan","family":"Umut","sequence":"first","affiliation":[{"name":"Department of Electronics and Automation, Corlu Vocational School, Tekirdag Namik Kemal University, Tekirdag 59850, T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9413-942X","authenticated-orcid":false,"given":"\u00dcmit Can","family":"Kumdereli","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering, Trakya University, Edirne 22030, T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,16]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2023, April 06). Deafness and Hearing Loss. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/deafness-and-hearing-loss."},{"key":"ref_2","first-page":"77","article-title":"Hearing loss, mental well-being and healthcare use: Results from the Health Survey for England (HSE)","volume":"42","author":"Crealey","year":"2018","journal-title":"J. Public Health"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/s13042-017-0705-5","article-title":"A review of hand gesture and sign language recognition techniques","volume":"10","author":"Cheok","year":"2019","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"135","DOI":"10.3390\/s150100135","article-title":"Sign Language Recognition with the Kinect Sensor Based on Conditional Random Fields","volume":"15","author":"Yang","year":"2015","journal-title":"Sensors"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zafrulla, Z., Sahni, H., Bedri, A., Thukral, P., and Starner, T. (2015, January 4\u20138). Hand detection in American Sign Language depth data using domain-driven random forest regression. Proceedings of the 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), Ljubljana, Slovenia.","DOI":"10.1109\/FG.2015.7163135"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Abdullahi, S.B., and Chamnongthai, K. (2022). American Sign Language Words Recognition of Skeletal Videos Using Processed Video Driven Multi-Stacked Deep LSTM. Sensors, 22.","DOI":"10.3390\/s22041406"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"15911","DOI":"10.1109\/ACCESS.2022.3148132","article-title":"American Sign Language Words Recognition Using Spatio-Temporal Prosodic and Angle Features: A Sequential Learning Approach","volume":"10","author":"Abdullahi","year":"2022","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"88511","DOI":"10.1109\/ACCESS.2023.3305255","article-title":"IDF-Sign: Addressing Inconsistent Depth Features for Dynamic Sign Word Recognition","volume":"11","author":"Abdullahi","year":"2023","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"123258","DOI":"10.1016\/j.eswa.2024.123258","article-title":"Spatial\u2013temporal feature-based End-to-end Fourier network for 3D sign language recognition","volume":"248","author":"Abdullahi","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"115213","DOI":"10.1016\/j.eswa.2021.115213","article-title":"ELM based two-handed dynamic Turkish Sign Language (TSL) word recognition","volume":"182","author":"Karakuzu","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chuan, C.H., Regina, E., and Guardino, C. (2014, January 3\u20136). American Sign Language Recognition Using Leap Motion Sensor. Proceedings of the 2014 13th International Conference on Machine Learning and Applications, Detroit, MI, USA.","DOI":"10.1109\/ICMLA.2014.110"},{"key":"ref_12","first-page":"1109","article-title":"American Sign Language Recognition Using Multidimensional Hidden Markov Models","volume":"22","author":"Wang","year":"2006","journal-title":"J. Inf. Sci. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1109\/THMS.2015.2406692","article-title":"Glove-Based Continuous Arabic Sign Language Recognition in User-Dependent Mode","volume":"45","author":"Tubaiz","year":"2015","journal-title":"IEEE Trans. Hum.-Mach. Syst."},{"key":"ref_14","unstructured":"Garg, A. (2012). Converting American Sign Language to Voice Using RBFNN. [Ph.D. Thesis, San Diego State University]."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1371","DOI":"10.1109\/34.735811","article-title":"Real-time american sign language recognition using desk and wearable computer-based video","volume":"20","author":"Starner","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Thepade, S.D., Kulkarni, G., Narkhede, A., Kelvekar, P., and Tathe, S. (2013, January 1\u20132). Sign language recognition using color means of gradient slope magnitude edge images. Proceedings of the 2013 International Conference on Intelligent Systems and Signal Processing (ISSP), Vallabh Vidyanagar, India.","DOI":"10.1109\/ISSP.2013.6526905"},{"key":"ref_17","unstructured":"Kim, T. (2016). American Sign Language fingerspelling recognition from video: Methods for unrestricted recognition and signer-independence. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Sadeddine, K., Chelali, F.Z., and Djeradi, R. (2015, January 25\u201327). Sign language recognition using PCA, wavelet and neural network. Proceedings of the 2015 3rd International Conference on Control, Engineering & Information Technology (CEIT), Tlemcen, Algeria.","DOI":"10.1109\/CEIT.2015.7233117"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1109\/TPAMI.2009.26","article-title":"Handling Movement Epenthesis and Hand Segmentation Ambiguities in Continuous Sign Language Recognition Using Nested Dynamic Programming","volume":"32","author":"Yang","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1109\/TPAMI.2022.3143074","article-title":"Towards Zero-Shot Sign Language Recognition","volume":"45","author":"Bilge","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1109\/TNNLS.2022.3174031","article-title":"MEN: Mutual Enhancement Networks for Sign Language Recognition and Education","volume":"35","author":"Liu","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1109\/THMS.2014.2318280","article-title":"Image-Based and Sensor-Based Approaches to Arabic Sign Language Recognition","volume":"44","author":"Mohandes","year":"2014","journal-title":"IEEE Trans. Hum.-Mach. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1109\/THMS.2022.3144000","article-title":"Sign Language Recognition Based on R (2+1) D With Spatial\u2013Temporal\u2013Channel Attention","volume":"52","author":"Han","year":"2022","journal-title":"IEEE Trans. Hum.-Mach. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2226","DOI":"10.1109\/ACCESS.2022.3233671","article-title":"Multi-Semantic Discriminative Feature Learning for Sign Gesture Recognition Using Hybrid Deep Neural Architecture","volume":"11","author":"Rajalakshmi","year":"2023","journal-title":"IEEE Access"},{"key":"ref_25","first-page":"26","article-title":"Static and Dynamic Isolated Indian and Russian Sign Language Recognition with Spatial and Temporal Feature Detection Using Hybrid Neural Network","volume":"22","author":"Rajalakshmi","year":"2022","journal-title":"ACM Trans. Asian Low-Resour. Lang. Inf. Process."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Paudyal, P., Banerjee, A., and Gupta, S.K.S. (2016, January 7\u201310). SCEPTRE: A Pervasive, Non-Invasive, and Programmable Gesture Recognition Technology. Proceedings of the 21st International Conference on Intelligent User Interfaces, Sonoma, CA, USA.","DOI":"10.1145\/2856767.2856794"},{"key":"ref_27","first-page":"41","article-title":"American Sign Language Recognition using Hidden Markov Models and Wearable Motion Sensors","volume":"10","author":"Fatmi","year":"2017","journal-title":"Trans. Mach. Learn. Data Min."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Savur, C., and Sahin, F. (2016, January 9\u201312). American Sign Language Recognition system by using surface EMG signal. Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Budapest, Hungary.","DOI":"10.1109\/SMC.2016.7844675"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Seddiqi, M., Kivrak, H., and Kose, H. (2020, January 15\u201317). Recognition of Turkish Sign Language (TID) Using sEMG Sensor. Proceedings of the 2020 Innovations in Intelligent Systems and Applications Conference (ASYU), \u0130stanbul, Turkey.","DOI":"10.1109\/ASYU50717.2020.9259859"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Yang, K., Qian, J., and Zhang, L. (2019). Real-Time Surface EMG Pattern Recognition for Hand Gestures Based on an Artificial Neural Network. Sensors, 19.","DOI":"10.3390\/s19143170"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Su, Z., and Yang, G. (2019, January 6\u20138). Real-Time Chinese Sign Language Recognition Based on Artificial Neural Networks. Proceedings of the 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), Dali, China.","DOI":"10.1109\/ROBIO49542.2019.8961641"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Jane, S.P.Y., and Sasidhar, S. (2018, January 12\u201315). Sign Language Interpreter: Classification of Forearm EMG and IMU Signals for Signing Exact English. Proceedings of the 2018 IEEE 14th International Conference on Control and Automation (ICCA), Anchorage, AK, USA.","DOI":"10.1109\/ICCA.2018.8444266"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1109\/JBHI.2016.2560907","article-title":"Chinese Sign Language Recognition Based on an Optimized Tree-Structure Framework","volume":"21","author":"Yang","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1224","DOI":"10.1109\/JSEN.2017.2779466","article-title":"Smart Wearable Hand Device for Sign Language Interpretation System with Sensors Fusion","volume":"18","author":"Lee","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Fatmi, R., Rashad, S., and Integlia, R. (2019, January 7\u20139). Comparing ANN, SVM, and HMM based Machine Learning Methods for American Sign Language Recognition using Wearable Motion Sensors. Proceedings of the 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA.","DOI":"10.1109\/CCWC.2019.8666491"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1109\/THMS.2022.3146787","article-title":"L-Sign: Large-Vocabulary Sign Gestures Recognition System","volume":"52","author":"Zheng","year":"2022","journal-title":"IEEE Trans. Hum.-Mach. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Madushanka, A.L.P., Senevirathne, R.G.D.C., Wijesekara, L.M.H., Arunatilake, S.M.K.D., and Sandaruwan, K.D. (2016, January 1\u20133). Framework for Sinhala Sign Language recognition and translation using a wearable armband. Proceedings of the 2016 Sixteenth International Conference on Advances in ICT for Emerging Regions (ICTer), Negombo, Sri Lanka.","DOI":"10.1109\/ICTER.2016.7829898"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1109\/72.182690","article-title":"Glove-Talk: A neural network interface between a data-glove and a speech synthesizer","volume":"4","author":"Fels","year":"1993","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3376","DOI":"10.1109\/TII.2017.2779814","article-title":"Feasibility of Wrist-Worn, Real-Time Hand, and Surface Gesture Recognition via sEMG and IMU Sensing","volume":"14","author":"Jiang","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1109\/TSMCA.2011.2116004","article-title":"A Framework for Hand Gesture Recognition Based on Accelerometer and EMG Sensors","volume":"41","author":"Zhang","year":"2011","journal-title":"IEEE Trans. Syst. Man Cybern.\u2014Part A Syst. Hum."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1109\/JBHI.2016.2598302","article-title":"A Wearable System for Recognizing American Sign Language in Real-Time Using IMU and Surface EMG Sensors","volume":"20","author":"Wu","year":"2016","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_42","first-page":"1","article-title":"Technical Features and Functionalities of Myo Armband: An Overview on Related Literature and Advanced Applications of Myoelectric Armbands Mainly Focused on Arm Prostheses","volume":"11","author":"Visconti","year":"2018","journal-title":"Int. J. Smart Sens. Intell. Syst."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Cognolato, M., Atzori, M., Faccio, D., Tiengo, C., Bassetto, F., Gassert, R., and Muller, H. (2018, January 26\u201329). Hand Gesture Classification in Transradial Amputees Using the Myo Armband Classifier. Proceedings of the 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), Enschede, The Netherlands.","DOI":"10.1109\/BIOROB.2018.8488106"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compbiomed.2016.03.023","article-title":"PSGMiner: A modular software for polysomnographic analysis","volume":"73","author":"Umut","year":"2016","journal-title":"Comput. Biol. Med."},{"key":"ref_45","unstructured":"Eibe, F., Hall, M.A., and Witten, I.H. (2016). The WEKA Workbench. Online Appendix for Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann. [4th ed.]."},{"key":"ref_46","unstructured":"Daniel, T.L., and Chantal, D.L. (2014). k Nearest Neighbor Algorithm. Discovering Knowledge in Data: An Introduction to Data Mining, Wiley."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/RBME.2020.3019769","article-title":"Wearable Sensor-Based Sign Language Recognition: A Comprehensive Review","volume":"14","author":"Kudrinko","year":"2021","journal-title":"IEEE Rev. Biomed. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/14\/4613\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:17:55Z","timestamp":1760109475000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/14\/4613"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,16]]},"references-count":47,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["s24144613"],"URL":"https:\/\/doi.org\/10.3390\/s24144613","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,16]]}}}