{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T03:02:18Z","timestamp":1761102138621,"version":"build-2065373602"},"reference-count":28,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2014,12,29]],"date-time":"2014-12-29T00:00:00Z","timestamp":1419811200000},"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>In this paper, we propose a system for inferring the pinch-to-zoom gesture using surface EMG (Electromyography) signals in real time. Pinch-to-zoom, which is a common gesture in smart devices such as an iPhone or an Android phone, is used to control the size of images or web pages according to the distance between the thumb and index finger. To infer the finger motion, we recorded EMG signals obtained from the first dorsal interosseous muscle, which is highly related to the pinch-to-zoom gesture, and used a support vector machine for classification between four finger motion distances. The powers which are estimated by Welch\u2019s method were used as feature vectors. In order to solve the multiclass classification problem, we applied a one-versus-one strategy, since a support vector machine is basically a binary classifier. As a result, our system yields 93.38% classification accuracy averaged over six subjects. The classification accuracy was estimated using 10-fold cross validation. Through our system, we expect to not only develop practical prosthetic devices but to also construct a novel user experience (UX) for smart devices.<\/jats:p>","DOI":"10.3390\/s150100394","type":"journal-article","created":{"date-parts":[[2014,12,29]],"date-time":"2014-12-29T10:58:34Z","timestamp":1419850714000},"page":"394-407","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["A Real-Time Pinch-to-Zoom Motion Detection by Means of a Surface EMG-Based Human-Computer Interface"],"prefix":"10.3390","volume":"15","author":[{"given":"Jongin","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Medical System Engineering (DMSE), Gwangju Institute of Science and Technology (GIST), Gwangju 500-712, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongrae","family":"Cho","sequence":"additional","affiliation":[{"name":"School of Mechatronics, Gwangju Institute of Science and Technology (GIST), Gwangju 500-712, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3761-8627","authenticated-orcid":false,"given":"Kwang","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Medical System Engineering (DMSE), Gwangju Institute of Science and Technology (GIST), Gwangju 500-712, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7233-5833","authenticated-orcid":false,"given":"Boreom","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Medical System Engineering (DMSE), Gwangju Institute of Science and Technology (GIST), Gwangju 500-712, Korea"},{"name":"School of Mechatronics, Gwangju Institute of Science and Technology (GIST), Gwangju 500-712, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2014,12,29]]},"reference":[{"key":"ref_1","first-page":"480","article-title":"EMG Signal Classification for Human Computer Interaction: Review","volume":"33","author":"Ahsan","year":"2009","journal-title":"Eur. J. Sci. Res."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Dementyev, A., and Paradiso, J.A. (2014, January 5\u20138). WristFlex: Low-power gesture input with wrist-worn pressure sensors. Honolulu, HI, USA.","DOI":"10.1145\/2642918.2647396"},{"key":"ref_3","first-page":"67","article-title":"Hand gestures for HCI using ICA of EMG","volume":"56","author":"Naik","year":"2006","journal-title":"HCSNet Workshop Use Vis. HCI"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1109\/TBME.2003.813539","article-title":"A robust, real-time control scheme for multifunction myoelectric control","volume":"50","author":"Englehart","year":"2003","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"54","DOI":"10.5607\/en.2010.19.1.54","article-title":"Finger Motion Decoding Using EMG Signals Corresponding Various Arm Postures","volume":"19","author":"You","year":"2010","journal-title":"Exp. Neurobiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1109\/10.914793","article-title":"A wavelet-based continuous classification scheme for multifunction myoelectric control","volume":"48","author":"Englehart","year":"2001","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1109\/TNSRE.2007.908376","article-title":"Real-Time Classification of Forearm Electromyographic Signals Corresponding to User-Selected Intentional Movements for Multifunction Prosthesis Control","volume":"15","author":"Momen","year":"2007","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1250","DOI":"10.1109\/TBME.2012.2232293","article-title":"Classification of Simultaneous Movements Using Surface EMG Pattern Recognition","volume":"60","author":"Young","year":"2013","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_9","first-page":"473","article-title":"Pattern Recognition of Eight Hand Motions Using Feature Extraction of Forearm EMG Signal","volume":"84","author":"Omari","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_10","first-page":"1016","article-title":"EMG pattern recognition by neural networks for multi fingers control","volume":"3","author":"Uchida","year":"1992","journal-title":"Eng. Med. Biol. Soc."},{"key":"ref_11","first-page":"1592","article-title":"EMG prosthetic hand controller discriminating ten motions using real-time learning method","volume":"3","author":"Nishikawa","year":"1999","journal-title":"Intell. Robot. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Nagata, K., Ando, K., Magatani, K., and Yamada, M. (2007, January 23\u201326). Development of the hand motion recognition system based on surface EMG using suitable measurement channels for pattern recognition. Lyon, France.","DOI":"10.1109\/IEMBS.2007.4353517"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1109\/TNSRE.2007.897025","article-title":"Characterization of Surface EMG Signal Based on Fuzzy Entropy","volume":"15","author":"Chen","year":"2007","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1109\/JBHI.2013.2249590","article-title":"Classification of Finger Movements for the Dexterous Hand Prosthesis Control with Surface Electromyography","volume":"17","author":"Bugmann","year":"2013","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Saponas, T.S., Tan, D.S., Morris, D., Balakrishnan, R., Turner, J., and Landay, J.A. (2009, January 4\u20137). Enabling always-available input with muscle-computer interfaces. Victoria, BC, Canada.","DOI":"10.1145\/1622176.1622208"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Saponas, T.S., Tan, D.S., Morris, D., Turner, J., and Landay, J.A. (2010, January 10\u201315). Making Muscle-Computer Interfaces More Practical. Atlanta, GA, USA.","DOI":"10.1145\/1753326.1753451"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.bspc.2012.08.005","article-title":"Pattern recognition of number gestures based on a wireless surface EMG system","volume":"8","author":"Chen","year":"2013","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1956","DOI":"10.1109\/TBME.2008.919734","article-title":"Support Vector Machine-Based Classification Scheme for Myoelectric Control Applied to Upper Limb","volume":"55","author":"Oskoei","year":"2008","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.1162\/089976603321891855","article-title":"Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel","volume":"15","author":"Keerthi","year":"2006","journal-title":"Neural Comput."},{"key":"ref_20","unstructured":"Day, S. Important Factors in Surface EMG Measurement. Available online: http:\/\/andrewsterian.com\/214\/EMG_measurement_and_recording.pdf."},{"key":"ref_21","unstructured":"Moon, I., Lee, M., Chu, J., and Mun, M. (2005, January 18\u201322). Wearable EMG-Based HCI for Electric-Powered Wheelchair Users with Motor Disabilities. Barcelona, Spain."},{"key":"ref_22","first-page":"2647","article-title":"A practical EMG-based human-computer interface for users with motor disabilities","volume":"37","author":"Barreto","year":"2000","journal-title":"Rehabil. Res. Dev."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2232","DOI":"10.1109\/TBME.2006.883695","article-title":"A real-time EMG pattern recognition system based on linear-nonlinear feature projection for a multifunction myoelectric hand","volume":"53","author":"Chu","year":"2006","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1801","DOI":"10.1109\/TBME.2005.856295","article-title":"A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses","volume":"52","author":"Huang","year":"2005","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Bitzer, S., and Smagt, P. Learning EMG control of a robotic hand: Towards active prostheses. 2819\u20132823.","DOI":"10.1109\/ROBOT.2006.1642128"},{"key":"ref_26","unstructured":"Yoshikawa, M., Mikawa, M., and Tanaka, K. (November, January 29). A myoelectric interface for robotic hand control using support vector machine. San Diego, CA, USA."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1109\/TNSRE.2009.2039590","article-title":"Multiple binary classifications via linear discriminant analysis for improved controllability of a powered prosthesis","volume":"18","author":"Hargrove","year":"2010","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2205","DOI":"10.1109\/TBME.2013.2250502","article-title":"Bilinear Modeling of EMG signals to extract user-independent features for multiuser Myoelectric Interface","volume":"60","author":"Matsubara","year":"2013","journal-title":"IEEE Trans. Biomed. 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