{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:50:11Z","timestamp":1760151011392,"version":"build-2065373602"},"reference-count":25,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,3]],"date-time":"2022-02-03T00:00:00Z","timestamp":1643846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation Program of China","award":["92059204","22090054","91850204"],"award-info":[{"award-number":["92059204","22090054","91850204"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In order to develop a non-contact and simple gesture recognition technology, a recognition method with a charge induction array of nine electrodes is proposed. Firstly, the principle of signal acquisition based on charge induction is introduced, and the whole system is given. Secondly, the recognition algorithms, including the pre-processing algorithm and back propagation neural network (BPNN) algorithm, are given to recognize three input modes of hand gestures, digital input, direction input and key input, respectively. Finally, experiments of three input modes of hand gestures are carried out, and the recognition accuracy is 97.2%, 94%, and 100% for digital input, direction input, and key input, respectively. The outstanding characteristic of this method is the real-time recognition of three hand gestures in the distance of 2 cm without the need of wearing any device, as well as being low cost and easy to implement.<\/jats:p>","DOI":"10.3390\/s22031158","type":"journal-article","created":{"date-parts":[[2022,2,6]],"date-time":"2022-02-06T20:40:18Z","timestamp":1644180018000},"page":"1158","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Gesture Recognition Method with a Charge Induction Array of Nine Electrodes"],"prefix":"10.3390","volume":"22","author":[{"given":"Hao","family":"Qian","sequence":"first","affiliation":[{"name":"School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yangbin","family":"Chi","sequence":"additional","affiliation":[{"name":"School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zining","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0933-8609","authenticated-orcid":false,"given":"Limin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Said, F.N., Yacoub, A., and Suen, C.Y. (1999, January 20\u201322). Recognition of English and Arabic numerals using a dynamic number of hidden neurons. Proceedings of the Fifth International Conference on Document Analysis and Recognition, ICDAR\u201999 (Cat. No.PR00318), Bangalore, India.","DOI":"10.1109\/ICDAR.1999.791768"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"11679","DOI":"10.1109\/ACCESS.2019.2891767","article-title":"Fully Convolutional Sequence Recognition Network for Water Meter Number Reading","volume":"7","author":"Yang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"208543","DOI":"10.1109\/ACCESS.2020.3039003","article-title":"HDSR-Flor: A Robust End-to-End System to Solve the Handwritten Digit String Recognition Problem in Real Complex Scenarios","volume":"8","author":"Neto","year":"2020","journal-title":"IEEE Access"},{"key":"ref_4","first-page":"2673","article-title":"A hybrid deep learning CNN-ELM model and its application in handwritten numeral recognition","volume":"11","author":"Guo","year":"2015","journal-title":"J. Comput. Inf. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2051","DOI":"10.1016\/S0031-3203(01)00203-5","article-title":"Handwritten numeral recognition using gradient and curvature of gray scale image","volume":"35","author":"Meng","year":"2002","journal-title":"Pattern Recognit."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Rajashekararadhya, S.V., and Ranjan, P.V. (2010, January 16\u201318). The Zone-Based Projection Distance Feature Extraction Method for Handwritten Numeral\/Mixed Numerals Recognition of Indian Scripts. Proceedings of the International Conference on Frontiers in Handwriting Recognition, ICFHR 2010, Kolkata, India.","DOI":"10.1109\/ICFHR.2010.101"},{"key":"ref_7","first-page":"83","article-title":"Kannada, Telugu and Devanagari Handwritten Numeral Recognition with Probabilistic Neural Network: A Novel Approach","volume":"26","author":"Dhandra","year":"2010","journal-title":"Int. J. Comput. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Abdulhussain, S.H., Mahmmod, B.M., Naser, M.A., Alsabah, M.Q., and Al-Haddad, S. (2021). A Robust Handwritten Numeral Recognition Using Hybrid Orthogonal Polynomials and Moments. Sensors, 21.","DOI":"10.3390\/s21061999"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Chen, Y., Xu, Z., Cai, S., Lang, Y., and Kuo, C.-C.J. (2018, January 24\u201327). A saak transform approach to efficient, scalable and robust handwritten digits recognition. Proceedings of the 2018 Picture Coding Symposium (PCS), San Francisco, CA, USA.","DOI":"10.1109\/PCS.2018.8456277"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"20502-1","DOI":"10.2352\/J.ImagingSci.Technol.2019.63.2.020502","article-title":"Multi-Language Handwritten Digits Recognition based on Novel Structural Features","volume":"63","author":"Alghazo","year":"2019","journal-title":"J. Imaging Sci. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1109\/TPAMI.2005.61","article-title":"Real-time gesture recognition by learning and selective control of visual interest points","volume":"27","author":"Kirishima","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1109\/TSMCB.2012.2217324","article-title":"Most Probable Longest Common Subsequence for Recognition of Gesture Character Input","volume":"43","author":"Frolova","year":"2013","journal-title":"IEEE Trans. Cybern."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1016\/S0262-8856(03)00070-2","article-title":"Hand gesture recognition using a real-time tracking method and hidden Markov models","volume":"21","author":"Chen","year":"2003","journal-title":"Image Vis. Comput."},{"key":"ref_14","unstructured":"Holt, G.A., Reinders, M., and Hendriks, E.A. (2007, January 13\u201315). Multi-dimensional dynamic time warping for gesture recognition. Proceedings of the Thirteenth Conference of the Advanced School for Computing & Imaging, Heijen, The Netherlands."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1007\/s00521-018-3775-8","article-title":"Jointly network: A network based on CNN and RBM for gesture recognition","volume":"31","author":"Cheng","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_16","first-page":"299","article-title":"Development of a Prototype for Non-contact Keyboard","volume":"Volume 876","author":"Kusano","year":"2018","journal-title":"Human Systems Engineering and Design, Proceedings of the 1st International Conference on Human Systems Engineering and Design (IHSED 2018): Future Trends and Applications, Reims, France, 25\u201327 October 2018"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1038\/s41928-020-00510-8","article-title":"A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition","volume":"4","author":"Moin","year":"2021","journal-title":"Nat. Electron."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.bspc.2018.07.010","article-title":"Robust hand gesture recognition with a double channel surface EMG wearable armband and SVM classifier","volume":"46","author":"Tavakoli","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Song, T.H., Jeong, S., Park, J.-H., Kwon, K.H., and Jeon, J.W. (2009, January 22\u201325). A non-contact input device using infrared sensor array. Proceedings of the IEEE International Conference on Robotics and Biomimetics, Bangkok, Thailand.","DOI":"10.1109\/ROBIO.2009.4912991"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Amin, M.G., Zeng, Z., and Shan, T. (2019, January 22\u201326). Hand Gesture Recognition based on Radar Micro-Doppler Signature Envelopes. Proceedings of the 2019 IEEE Radar Conference (RadarConf19), Boston, MA, USA.","DOI":"10.1109\/RADAR.2019.8835661"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zeng, Q., Kuang, Z., Wu, S., and Yang, J. (2019). A method of ultrasonic finger gesture recognition based on the micro-doppler effect. Appl. Sci., 9.","DOI":"10.3390\/app9112314"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1109\/JSEN.2019.2940250","article-title":"A Recognition Method for Hand Motion Direction Based on Charge Induction","volume":"20","author":"Wang","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_23","first-page":"36","article-title":"Face recognition system based on principal component analysis (PCA) with back propagation neural networks (BPNN)","volume":"2","author":"Kashem","year":"2011","journal-title":"Can. J. Image Process. Comput. Vis."},{"key":"ref_24","first-page":"800","article-title":"Fingerprint verification based on invariant moment features and nonlinear BPNN","volume":"6","author":"Yang","year":"2008","journal-title":"Int. J. Control. Autom. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"178820","DOI":"10.1016\/j.tca.2020.178820","article-title":"Prediction of temperature and CO concentration fields based on BPNN in low-temperature coal oxidation","volume":"695","author":"Zhao","year":"2021","journal-title":"Thermochim. Acta"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/1158\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:13:24Z","timestamp":1760134404000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/1158"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,3]]},"references-count":25,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["s22031158"],"URL":"https:\/\/doi.org\/10.3390\/s22031158","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,2,3]]}}}