{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T18:16:30Z","timestamp":1770747390079,"version":"3.49.0"},"reference-count":27,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2016,4,19]],"date-time":"2016-04-19T00:00:00Z","timestamp":1461024000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation of China","award":["61271138"],"award-info":[{"award-number":["61271138"]}]},{"name":"National Nature Science Foundation of China","award":["61431017"],"award-info":[{"award-number":["61431017"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sign language recognition (SLR) can provide a helpful tool for the communication between the deaf and the external world. This paper proposed a component-based vocabulary extensible SLR framework using data from surface electromyographic (sEMG) sensors, accelerometers (ACC), and gyroscopes (GYRO). In this framework, a sign word was considered to be a combination of five common sign components, including hand shape, axis, orientation, rotation, and trajectory, and sign classification was implemented based on the recognition of five components. Especially, the proposed SLR framework consisted of two major parts. The first part was to obtain the component-based form of sign gestures and establish the code table of target sign gesture set using data from a reference subject. In the second part, which was designed for new users, component classifiers were trained using a training set suggested by the reference subject and the classification of unknown gestures was performed with a code matching method. Five subjects participated in this study and recognition experiments under different size of training sets were implemented on a target gesture set consisting of 110 frequently-used Chinese Sign Language (CSL) sign words. The experimental results demonstrated that the proposed framework can realize large-scale gesture set recognition with a small-scale training set. With the smallest training sets (containing about one-third gestures of the target gesture set) suggested by two reference subjects, (82.6 \u00b1 13.2)% and (79.7 \u00b1 13.4)% average recognition accuracy were obtained for 110 words respectively, and the average recognition accuracy climbed up to (88 \u00b1 13.7)% and (86.3 \u00b1 13.7)% when the training set included 50~60 gestures (about half of the target gesture set). The proposed framework can significantly reduce the user\u2019s training burden in large-scale gesture recognition, which will facilitate the implementation of a practical SLR system.<\/jats:p>","DOI":"10.3390\/s16040556","type":"journal-article","created":{"date-parts":[[2016,4,19]],"date-time":"2016-04-19T06:32:53Z","timestamp":1461047573000},"page":"556","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["A Component-Based Vocabulary-Extensible Sign Language Gesture Recognition Framework"],"prefix":"10.3390","volume":"16","author":[{"given":"Shengjing","family":"Wei","sequence":"first","affiliation":[{"name":"Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xidong","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuai","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1533-4340","authenticated-orcid":false,"given":"Xu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,4,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"23303","DOI":"10.3390\/s150923303","article-title":"A Novel Phonology-and Radical-Coded Chinese Sign Language Recognition Framework Using Accelerometer and Surface Electromyography Sensors","volume":"15","author":"Cheng","year":"2015","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1109\/TSMCA.2004.824852","article-title":"Large vocabulary sign language recognition based on fuzzy decision trees","volume":"34","author":"Fang","year":"2004","journal-title":"IEEE Trans. Syst. Man Cybern. Part A Syst. Hum."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1109\/TPAMI.2005.112","article-title":"Automatic sign language analysis: A survey and the future beyond lexical meaning","volume":"27","author":"Ong","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1826","DOI":"10.1016\/j.imavis.2009.02.005","article-title":"Modelling and recognition of the linguistic components in American sign language","volume":"27","author":"Ding","year":"2009","journal-title":"Image Vis. Comput."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kelly, D., Reilly Delannoy, J., Mc Donald, J., and Markham, C. (2009, January 2\u20134). A framework for continuous multimodal sign language recognition. Proceedings of the 11th International Conference on Multimodal Interfaces (ICMI 2009), Cambridge, MA, USA.","DOI":"10.1145\/1647314.1647387"},{"key":"ref_6","unstructured":"Lim, I., Lu, J., Ng, C., Ong, T., and Ong, C. (2015, January 2\u20134). Sign-language Recognition through Gesture & Movement Analysis. Proceedings of the DLSU Research Congress, Manila, Philippines."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"28646","DOI":"10.3390\/s151128646","article-title":"HAGR-D: A Novel Approach for Gesture Recognition with Depth Maps","volume":"15","author":"Santos","year":"2015","journal-title":"Sensors"},{"key":"ref_8","unstructured":"Camastra, F., and De, F.D. (2012, January 17\u201319). LVQ-based hand gesture recognition using a data glove. Proceedings of the 22th Italian Workshop on Neural Networks, 2012, Vietri sul Mare, Salerno, Italy."},{"key":"ref_9","unstructured":"Dong, C., Leu, M., and Yin, Z. (2015, January 7\u201312). American Sign Language Alphabet Recognition Using Microsoft Kinect. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, USA."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1418","DOI":"10.1109\/TCYB.2013.2265337","article-title":"Discriminative exemplar coding for sign language recognition with Kinect","volume":"43","author":"Sun","year":"2013","journal-title":"IEEE Trans. Cybern."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Su, R., Chen, X., Cao, S., and Zhang, X. (2016). Random Forest-Based Recognition of Isolated Sign Language Subwords Using Data from Accelerometers and Surface Electromyographic Sensors. Sensors, 16.","DOI":"10.3390\/s16010100"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.bspc.2007.07.009","article-title":"Myoelectric control systems\u2014A survey","volume":"2","author":"Oskoei","year":"2007","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_13","unstructured":"Li, Y., Chen, X., Zhang, X., Wang, K., and Yang, J. (September, January 30). Interpreting sign components from accelerometer and sEMG data for automatic sign language recognition. Proceedings of IEEE Engineering in Medicine and Biology Society, EMBC, Boston, MA, USA."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1109\/THMS.2014.2302794","article-title":"A hand gesture recognition framework and wearable gesture-based interaction prototype for mobile devices","volume":"44","author":"Lu","year":"2014","journal-title":"IEEE Trans. Hum. Mach. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1016\/j.pmcj.2009.07.007","article-title":"uWave: Accelerometer-based personalized gesture recognition and its applications","volume":"5","author":"Liu","year":"2009","journal-title":"Pervasive Mob. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2695","DOI":"10.1109\/TBME.2012.2190734","article-title":"A sign-component-based framework for Chinese sign language recognition using accelerometer and sEMG data","volume":"59","author":"Li","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2879","DOI":"10.1109\/TBME.2009.2013200","article-title":"Sign language recognition using intrinsic-mode sample entropy on sEMG and accelerometer data","volume":"56","author":"Kosmidou","year":"2009","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wu, J., Tian, Z., Sun, L., Estevez, L., and Jafari, R. (2015, January 9\u201312). Real-time American Sign Language Recognition using wrist-worn motion and surface EMG sensors. Proceedings of the IEEE 12th International Conference, Wearable and Implantable Body Sensor Networks (BSN), Cambridge, MA, USA.","DOI":"10.1109\/BSN.2015.7299393"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hoffman, M., Varcholik, P., and LaViola, J.J.J. (2010, January 20\u201324). Breaking the status quo: Improving 3D gesture recognition with spatially convenient input devices. Proceedings of the IEEE Virtual Reality Conference, Waltham, MA, USA.","DOI":"10.1109\/VR.2010.5444813"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez-Camarena, M., Oramas, M.J., and Tuytelaars, T. (2015, January 27\u201330). Towards sign language recognition based on body parts relations. Proceedings of the IEEE International Conference, Image Processing (ICIP), Quebec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7351243"},{"key":"ref_21","unstructured":"Fang, G., Gao, X., Gao, W., and Chen, Y. (2004, January 26\u201326). A novel approach to automatically extracting basic units from Chinese sign language. Proceedings of the 17th International Conference on Pattern Recognition, ICPR, Cambridge, UK."},{"key":"ref_22","unstructured":"Wang, C., Gao, W., and Shan, S. (2002, January 20\u201321). An approach based on phonemes to large vocabulary Chinese sign language recognition. Proceedings of the fifth IEEE International Conference on Automatic Face and Gesture Recognition, Washington, DC, USA."},{"key":"ref_23","unstructured":"Duda, R.O., Hart, P.E., and Stork, D.G. (2001). Pattern Classification, Wiley Interscience Publication. [2nd ed.]."},{"key":"ref_24","unstructured":"Phinyomark, A., Hirunviriya, S., Limsakul, C., and Phukpattaranont, P. (2010, January 19\u201320). Evaluation of EMG feature extraction for hand movement recognition based on Euclidean distance and standard deviation. Proceedings of the IEEE International Conference, Electrical Engineering\/Electronics Computer Telecommunications and Information Technology (ECTI-CON), Chiangmai, Thailand."},{"key":"ref_25","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. Part A Syst. Hum."},{"key":"ref_26","unstructured":"Rabiner, L., and Jaung, B. (1993). Fundamentals of Speech Recognition, Prentice-Hall. [1st ed.]."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3475","DOI":"10.1109\/JSEN.2015.2392091","article-title":"Similarity matching-based extensible hand gesture recognition","volume":"15","author":"Xie","year":"2015","journal-title":"IEEE Sens. J."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/4\/556\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:22:28Z","timestamp":1760210548000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/4\/556"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,4,19]]},"references-count":27,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2016,4]]}},"alternative-id":["s16040556"],"URL":"https:\/\/doi.org\/10.3390\/s16040556","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,4,19]]}}}