{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T16:39:37Z","timestamp":1778344777721,"version":"3.51.4"},"reference-count":37,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,6,2]],"date-time":"2020-06-02T00:00:00Z","timestamp":1591056000000},"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, a customizable wearable 3D-printed bionic arm is designed, fabricated, and optimized for a right arm amputee. An experimental test has been conducted for the user, where control of the artificial bionic hand is accomplished successfully using surface electromyography (sEMG) signals acquired by a multi-channel wearable armband. The 3D-printed bionic arm was designed for the low cost of 295 USD, and was lightweight at 428 g. To facilitate a generic control of the bionic arm, sEMG data were collected for a set of gestures (fist, spread fingers, wave-in, wave-out) from a wide range of participants. The collected data were processed and features related to the gestures were extracted for the purpose of training a classifier. In this study, several classifiers based on neural networks, support vector machine, and decision trees were constructed, trained, and statistically compared. The support vector machine classifier was found to exhibit an 89.93% success rate. Real-time testing of the bionic arm with the optimum classifier is demonstrated.<\/jats:p>","DOI":"10.3390\/s20113144","type":"journal-article","created":{"date-parts":[[2020,6,2]],"date-time":"2020-06-02T09:19:27Z","timestamp":1591089567000},"page":"3144","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Machine-Learning-Based Muscle Control of a 3D-Printed Bionic Arm"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7380-6238","authenticated-orcid":false,"given":"Sherif","family":"Said","sequence":"first","affiliation":[{"name":"College of Engineering and Technology, American University of the Middle East, Al-Eqaila 54200, Kuwait"},{"name":"University-Paris-Est, LiSSi, (UPEC), 94400 Vitry-sur-Seine, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ilyes","family":"Boulkaibet","sequence":"additional","affiliation":[{"name":"College of Engineering and Technology, American University of the Middle East, Al-Eqaila 54200, Kuwait"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Murtaza","family":"Sheikh","sequence":"additional","affiliation":[{"name":"College of Engineering and Technology, American University of the Middle East, Al-Eqaila 54200, Kuwait"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0044-996X","authenticated-orcid":false,"given":"Abdullah S.","family":"Karar","sequence":"additional","affiliation":[{"name":"College of Engineering and Technology, American University of the Middle East, Al-Eqaila 54200, Kuwait"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6618-4939","authenticated-orcid":false,"given":"Samer","family":"Alkork","sequence":"additional","affiliation":[{"name":"College of Engineering and Technology, American University of the Middle East, Al-Eqaila 54200, Kuwait"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amine","family":"Nait-ali","sequence":"additional","affiliation":[{"name":"University-Paris-Est, LiSSi, (UPEC), 94400 Vitry-sur-Seine, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1109\/TPAMI.2002.1023805","article-title":"Human Activity Recognition Using Multidimensional Indexing","volume":"24","author":"Wang","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_2","unstructured":"Du, W., and Li, H. (2000, January 21\u201325). Vision based gesture recognition system with single camera. Proceedings of the International Conference on Signal Processing, Beijing, China."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kapoor, A., and Picard, R. (2001, January 15\u201316). A Real-Time Head Nod and Shake Detector. Proceedings of the Workshop Perspective User Interfaces, Orlando, FL, USA.","DOI":"10.1145\/971478.971509"},{"key":"ref_4","unstructured":"Matsumoto, Y., and Zelinsky, A. (2000, January 28\u201330). An algorithm for real-time stereo vision implementation of head pose and gaze direction measurement. Proceedings of the International Conference on Automatic Face and Gesture Recognition, Grenoble, France."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Morency, L., Sidner, C., Lee, C., and Darrell, T. (2005, January 4\u20136). Contextual Recognition of Head Gestures. Proceedings of the International Conference on Multimodal Interfaces, Torento, Italy.","DOI":"10.1145\/1088463.1088470"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Morimoto, C., Yacoob, Y., and Devis, L. (1996, January 25\u201329). Recognition of Head Gestures Using Hidden Markov Models. Proceedings of the International Conference on Pattern Recognition, Vienna, Austria.","DOI":"10.1109\/ICPR.1996.546990"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Nickel, K., and Stiefelhagen, R. (2003, January 5\u20137). Pointing Gesture Recognition on 3DTracking of Face Hands and Head Orientation. Proceedings of the International Conference on Multimodal Interfaces, Vancouver, BC, Canada.","DOI":"10.1145\/958432.958460"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Xiao, R., and Harrison, C. (2016, January 16\u201319). Advancing hand gesture recognition with high resolution electrical impedance tomography. Proceedings of the 29th Annual Symposium on User Interface Software and Technology, Tokyo, Japan.","DOI":"10.1145\/2984511.2984574"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Saponas, T., Tan, S., Morris, D., and Balakrishnan, R. (2008, January 5\u201310). Demonstrating the feasibility of using forearm electromyography for muscle\u2013computer interfaces. Proceedings of the 26th SIGCHI Conference on Human Factors in Computing Systems, Florence, Italy.","DOI":"10.1145\/1357054.1357138"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1109\/TSMCC.2006.875418","article-title":"Gesture-based control and EMG decomposition","volume":"36","author":"Wheeler","year":"2006","journal-title":"IEEE Trans. Syst. Man Cybern. C Appl. Rev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1109\/TNSRE.2012.2218832","article-title":"A novel myoelectric pattern recognition strategy for hand function restoration after incomplete cervical spinal cord injury","volume":"21","author":"Liu","year":"2013","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1804","DOI":"10.1109\/TNSRE.2011.2182525","article-title":"Quantification of feature space changes with experience during electromyogram pattern recognition control","volume":"20","author":"Bunderson","year":"2012","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_13","unstructured":"Li, X., Fu, J., Xiong, L., Shi, Y., Davoodi, R., and Li, Y. (2015, January 14\u201316). Identification of finger force and motion from forearm surface electromyography. Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), San Diego, CA, USA."},{"key":"ref_14","first-page":"1","article-title":"Real-time intelligent pattern recognition algorithm for surface EMG signals","volume":"6","author":"Khezrim","year":"2007","journal-title":"Biomed. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1255","DOI":"10.1109\/TBME.2003.818469","article-title":"A fuzzy clustering neural network architecture for multifunction upperlimb prosthesis","volume":"50","author":"Karlik","year":"2003","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_16","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_17","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_18","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.jneumeth.2018.10.004","article-title":"EMG and ENG-envelope pattern recognition for prosthetic hand control","volume":"311","author":"Noce","year":"2019","journal-title":"J. Neurosci. Methods"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.bbe.2017.11.001","article-title":"A bionic hand controlled by hand gesture recognition based on surface EMG signals: A preliminary study","volume":"38","author":"Shi","year":"2018","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Said, S., Alkork, S., Beyrouthy, T., and Fayek, M. (September, January 30). Wearable Bio-Sensors Bracelet for Driver\u2019s Health Emergency Detection. Proceedings of the 2017 2nd International Conference on Bio-Engineering for Smart Technologies (BioSMART), Paris, France.","DOI":"10.1109\/BIOSMART.2017.8095335"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Rawat, S., Vats, S., and Kumar, P. (2016, January 25\u201327). Evaluating and Exploring the MYO Armband. Proceedings of the International Conference on System Modeling & Advancement in Research Trends, Moradabad, India.","DOI":"10.1109\/SYSMART.2016.7894501"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Said, S., Sheikh, M., Al-Rashidi, F., Lakys, Y., Beyrouthy, T., and Naitali, A. (2019, January 24\u201326). A Customizable Wearable Robust 3D Printed Bionic Arm: Muscle Controlled. Proceedings of the 3rd International Conference on Bio-Engineering for Smart Technologies (BioSMART), Paris, France.","DOI":"10.1109\/BIOSMART.2019.8734266"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13104-015-0971-9","article-title":"Cyborg beast: A low-cost 3d-printed prosthetic hand for children with upper-limb differences","volume":"8","author":"Zuniga","year":"2015","journal-title":"BMC Res. Notes"},{"key":"ref_24","unstructured":"McGimpsy, G., and Bradford, T. (2010). C: Limb Prosthetics Services and Devices Critical Unmet Need: Market Analysis, Bioengineering Institute Center for Neuroproshetics, Worcester Polytechnic Institution."},{"key":"ref_25","unstructured":"(2019, April 15). Available online: https:\/\/openbionicslabs.com\/shop\/chestnut-board."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1006\/mssp.1996.0001","article-title":"A comparison of autoregressive modeling techniques for fault diagnosis of rolling element bearings","volume":"10","author":"Baillie","year":"1996","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1028","DOI":"10.1016\/j.ymssp.2010.08.014","article-title":"Operational modal analysis by updating autoregressive model","volume":"25","author":"Vu","year":"2011","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"11498","DOI":"10.1016\/j.ifacol.2017.08.1602","article-title":"A testing system for a real-time gesture classification using surface EMG","volume":"50","author":"Akhmadeev","year":"2018","journal-title":"IFAC-PapersOnLine"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ahsan, M.R., Ibrahimy, M.I., and Khalifa, O.O. (2011, January 17\u201319). Electromygraphy (EMG) signal based HGR using artificial neural network (ANN). Proceedings of the 4th International Conference on Mechatronics (ICOM), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICOM.2011.5937135"},{"key":"ref_30","unstructured":"Zhang, X.H., Wang, J.J., Wang, X., and Ma, X.L. (2016, January 24\u201326). Improvement of Dynamic HGR Based on HMM Algorithm. Proceedings of the International Conference on Information System and Artificial Intelligence (ISAI) Hong Kong, Hong Kong, China."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Dai, Y., Zhou, Z., Chen, X., and Yang, Y. (2017, January 6\u20139). A novel method for simultaneous gesture segmentation and recognition based on HMM. Proceedings of the 2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), Xiamen, China.","DOI":"10.1109\/ISPACS.2017.8266564"},{"key":"ref_32","unstructured":"Weston, J., and Watkins, C. (1999, January 21\u201323). Multi-class support vector machines. Proceedings of the European Symp. Artificial Neural Networks (ESANN), Bruges, Belgium."},{"key":"ref_33","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, 14.","DOI":"10.3390\/s19143170"},{"key":"ref_34","first-page":"1","article-title":"Using hierarchical time series clustering algorithm and wavelet classifier for biometric voice classification","volume":"2012","author":"Fong","year":"2012","journal-title":"J. Biomed. Biotechnol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Theodoridis, S. (2015). Machine Learning: A Bayesian and Optimization Perspective, Academic Press.","DOI":"10.1016\/B978-0-12-801522-3.00012-4"},{"key":"ref_36","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 Syst. Hum."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Marsland, S. (2014). Machine Learning: An Algorithmic Perspective, Chapman and Hall\/CRC.","DOI":"10.1201\/b17476"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/11\/3144\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:34:55Z","timestamp":1760175295000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/11\/3144"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,2]]},"references-count":37,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["s20113144"],"URL":"https:\/\/doi.org\/10.3390\/s20113144","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,2]]}}}