{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T19:29:05Z","timestamp":1769196545595,"version":"3.49.0"},"reference-count":40,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,10,9]],"date-time":"2020-10-09T00:00:00Z","timestamp":1602201600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotics"],"abstract":"<jats:p>This article sought to address issues related to human-robot cooperation tasks focusing especially on robotic operation using bio-signals. In particular, we propose to develop a control scheme for a robot arm based on electromyography (EMG) signal that allows a cooperative task between humans and robots that would enable teleoperations. A basic framework for achieving the task and conducting EMG signals analysis of the motion of upper limb muscles for mapping the hand motion is presented. The objective of this work is to investigate the application of a wearable EMG device to control a robot arm in real-time. Three EMG sensors are attached to the brachioradialis, biceps brachii, and anterior deltoid muscles as targeted muscles. Three motions were conducted by moving the arm about the elbow joint, shoulder joint, and a combination of the two joints giving a two degree of freedom. Five subjects were used for the experiments. The results indicated that the performance of the system had an overall accuracy varying from 50% to 100% for the three motions for all subjects. This study has further shown that upper-limb motion discrimination can be used to control the robotic manipulator arm with its simplicity and low computational cost.<\/jats:p>","DOI":"10.3390\/robotics9040083","type":"journal-article","created":{"date-parts":[[2020,10,9]],"date-time":"2020-10-09T10:19:23Z","timestamp":1602238763000},"page":"83","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Mapping Three Electromyography Signals Generated by Human Elbow and Shoulder Movements to Two Degree of Freedom Upper-Limb Robot Control"],"prefix":"10.3390","volume":"9","author":[{"given":"Pringgo Widyo","family":"Laksono","sequence":"first","affiliation":[{"name":"Graduate School of Engineering, Gifu University, Gifu 501-1193, Japan"},{"name":"Industrial Engineering, Faculty of Engineering, Universitas Sebelas Maret, Surakarta 57126, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kojiro","family":"Matsushita","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering, Gifu University, Gifu 501-1193, Japan"},{"name":"Intelligent Production Technology Research &amp; Development Center for Aerospace (IPTeCA), Tokai National Higher Education and Research System, Gifu 501-1193, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8738-913X","authenticated-orcid":false,"given":"Muhammad Syaiful Amri bin","family":"Suhaimi","sequence":"additional","affiliation":[{"name":"National Institute of Technology, Gifu College, Gifu 501-0495, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5133-8237","authenticated-orcid":false,"given":"Takahide","family":"Kitamura","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering, Gifu University, Gifu 501-1193, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Waweru","family":"Njeri","sequence":"additional","affiliation":[{"name":"Intelligent Production Technology Research &amp; Development Center for Aerospace (IPTeCA), Tokai National Higher Education and Research System, Gifu 501-1193, Japan"},{"name":"School of Engineering, Dedan Kimathi University of Technology, Private Bag, Nyeri 10143, Kenya"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3343-3614","authenticated-orcid":false,"given":"Joseph","family":"Muguro","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering, Gifu University, Gifu 501-1193, Japan"},{"name":"School of Engineering, Dedan Kimathi University of Technology, Private Bag, Nyeri 10143, Kenya"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7806-8492","authenticated-orcid":false,"given":"Minoru","family":"Sasaki","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering, Gifu University, Gifu 501-1193, Japan"},{"name":"Intelligent Production Technology Research &amp; Development Center for Aerospace (IPTeCA), Tokai National Higher Education and Research System, Gifu 501-1193, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,9]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"A review on EMG-based motor intention prediction of continuous human upper limb motion for human-robot collaboration","volume":"51","author":"Feleke","year":"2019","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s12553-019-00302-x","article-title":"Robot technology for future welfare: Meeting upcoming societal challenges\u2014An outlook with offset in the development in Scandinavia","volume":"9","author":"Bodenhagen","year":"2019","journal-title":"Health Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1016\/j.dsx.2020.04.032","article-title":"Industry 4.0 technologies and their applications in fighting COVID-19 pandemic","volume":"14","author":"Javaid","year":"2020","journal-title":"Diabetes Metab. Syndr. Clin. Res. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.cirp.2017.04.101","article-title":"Human-robot collaborative assembly in cyber-physical production: Classification framework and implementation","volume":"66","author":"Wang","year":"2017","journal-title":"CIRP Ann. Manuf. Technol."},{"key":"ref_5","first-page":"181","article-title":"An adaptive upper-arm EMG-based robot control system","volume":"12","author":"Liu","year":"2010","journal-title":"Int. J. Fuzzy Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/TSMCB.2010.2045120","article-title":"A switching regime model for the emg-based control of a robot arm","volume":"41","author":"Artemiadis","year":"2011","journal-title":"IEEE Trans. Syst. Man Cybern. Part B Cybern."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1109\/TRO.2018.2885464","article-title":"Intuitive Adaptive Orientation Control for Enhanced Human-Robot Interaction","volume":"35","author":"Vu","year":"2019","journal-title":"IEEE Trans. Robot."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1109\/TITB.2010.2040832","article-title":"An EMG-based robot control scheme robust to time-varying EMG signal features","volume":"14","author":"Artemiadis","year":"2010","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Tsuji, T., Shibanoki, T., and Shima, K. (2015). EMG-Based Control of a Multi-Joint Robot for Operating a Glovebox. Handb. Res. Adv. Robot. Mechatronics, 36\u201352.","DOI":"10.4018\/978-1-4666-7387-8.ch003"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"9640","DOI":"10.1109\/JSEN.2018.2872650","article-title":"A Novel 6-D Tracking Method by Fusion of 5-D Magnetic Tracking and 3-D Inertial Sensing","volume":"18","author":"Dai","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Meattini, R., Benatti, S., Scarcia, U., de Gregorio, D., Benini, L., and Melchiorri, C. (2018). An sEMG-Based Human-Robot Interface for Robotic Hands Using Machine Learning and Synergies. IEEE Trans. Compon. Packaging Manuf. Technol., 1\u201310.","DOI":"10.1109\/TCPMT.2018.2799987"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rcim.2015.12.007","article-title":"Collaborative manufacturing with physical human-robot interaction","volume":"40","author":"Cherubini","year":"2016","journal-title":"Robot. Comput. Integr. Manuf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"861","DOI":"10.3233\/JIFS-171562","article-title":"EMG and IMU based real-time HCI using dynamic hand gestures for a multiple-DoF robot arm","volume":"35","author":"Shin","year":"2018","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"030034","DOI":"10.1063\/5.0000542","article-title":"Preliminary Research of Surface Electromyogram (sEMG) Signal Analysis for Robotic Arm Control","volume":"2217","author":"Laksono","year":"2020","journal-title":"AIP Conf. Proc."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"020008","DOI":"10.1063\/5.0000624","article-title":"Robot control systems using bio-potential signals Robot Control Systems Using Bio-Potential Signals","volume":"2217","author":"Sasaki","year":"2020","journal-title":"AIP Conf. Proc."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.mechatronics.2018.02.009","article-title":"Survey on human\u2013robot collaboration in industrial settings: Safety, intuitive interfaces and applications","volume":"55","author":"Villani","year":"2018","journal-title":"Mechatronics"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Benatti, S., Milosevic, B., Farella, E., Gruppioni, E., and Benini, L. (2017). A Prosthetic Hand Body Area Controller Based on Efficient Pattern Recognition Control Strategies. Sensors, 17.","DOI":"10.3390\/s17040869"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Fukuda, O., Tsuji, T., Kaneko, M., and Otsuka, A. (2003). A human-assisting manipulator teleoperated by EMG signals and arm motions. IEEE Trans. Robot. Autom.","DOI":"10.1109\/TRA.2003.808873"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1109\/TRO.2009.2039378","article-title":"EMG-based control of a robot arm using low-dimensional embeddings","volume":"26","author":"Artemiadis","year":"2010","journal-title":"IEEE Trans. Robot."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"101791","DOI":"10.1016\/j.bspc.2019.101791","article-title":"Novel algorithm for conventional myocontrol of upper limbs prosthetics","volume":"57","author":"Benchabane","year":"2020","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3597","DOI":"10.1109\/TLA.2016.7786339","article-title":"Robotic Arm Activation using Surface Electromyography with LABVIEW","volume":"14","author":"Junior","year":"2016","journal-title":"IEEE Lat. Am. Trans."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Nazmi, N., Rahman, M.A.A., Yamamoto, S.I., Ahmad, S.A., Zamzuri, H., and Mazlan, S.A. (2016). A review of classification techniques of EMG signals during isotonic and isometric contractions. Sensors, 16.","DOI":"10.3390\/s16081304"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Parajuli, N., Sreenivasan, N., Bifulco, P., Cesarelli, M., Savino, S., Niola, V., Esposito, D., Hamilton, T.J., Naik, G.R., and Gunawardana, U. (2019). Real-time EMG based pattern recognition control for hand prostheses: A review on existing methods, challenges and future implementation. Sensors, 19.","DOI":"10.3390\/s19204596"},{"key":"ref_24","unstructured":"Turker, H. (2013). Recent Trends in EMG-Based Control Methods for Assistive Robots. Electrodiagnosis in New Frontiers of Clinical Research, Available online: https:\/\/www.intechopen.com\/books\/electrodiagnosis-in-new-frontiers-of-clinical-research."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"39564","DOI":"10.1109\/ACCESS.2019.2906584","article-title":"A Review on Electromyography Decoding and Pattern Recognition for Human-Machine Interaction","volume":"7","author":"Simao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1743-0003-11-5","article-title":"A comparison of the real-time controllability of pattern recognition to conventional myoelectric control for discrete and simultaneous movements","volume":"11","author":"Young","year":"2014","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_27","first-page":"378","article-title":"Teleoperated robotic arm movement using electromyography signal with wearable Myo armband","volume":"32","author":"Hassan","year":"2019","journal-title":"J. King Saud. Univ. Eng. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"7420","DOI":"10.1016\/j.eswa.2012.01.102","article-title":"Feature reduction and selection for EMG signal classification","volume":"39","author":"Phinyomark","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/TNSRE.2015.2410176","article-title":"Real-time task discrimination for myoelectric control employing task-specific muscle synergies","volume":"24","author":"Rasoo","year":"2016","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Jaramillo-Y\u00e1nez, A., Benalc\u00e1zar, M.E., and Mena-Maldonado, E. (2020). Real-time hand gesture recognition using surface electromyography and machine learning: A systematic literature review. Sensors, 20.","DOI":"10.3390\/s20092467"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Sharma, S., and Dubey, A.K. (2012, January 17\u201319). Movement control of robot in real time using EMG signal. Proceedings of the 2012 2nd International Conference on Power, Control and Embedded Systems (ICPCES 2012), Allahabad, India.","DOI":"10.1109\/ICPCES.2012.6508060"},{"key":"ref_32","first-page":"178","article-title":"Dynamic feature for an effective elbow-joint angle estimation based on electromyography signals","volume":"19","author":"Triwiyanto","year":"2020","journal-title":"Indones. J. Electr. Eng. Comput. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"10150","DOI":"10.1109\/ACCESS.2019.2891350","article-title":"Intelligent EMG pattern recognition control method for upper-limb multifunctional prostheses: Advances, current challenges, and future prospects","volume":"7","author":"Samuel","year":"2019","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1109\/TBME.2006.889192","article-title":"A comparison of surface and intramuscular myoelectric signal classification","volume":"54","author":"Hargrove","year":"2007","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Pons, J.L. (2008). Wearable Robots: Biomechatronic Exoskeletons, John Wiley & Sons.","DOI":"10.1002\/9780470987667"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2185","DOI":"10.1007\/s12541-014-0580-x","article-title":"Human shoulder motion extraction using EMG signals","volume":"15","author":"Jang","year":"2014","journal-title":"Int. J. Precis. Eng. Manuf."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Phinyomark, A., Khushaba, R.N., and Scheme, E. (2018). Feature extraction and selection for myoelectric control based on wearable EMG sensors. Sensors, 18.","DOI":"10.3390\/s18051615"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Bin Suhaimi, M.S.A., Matsushita, K., Sasaki, M., and Njeri, W. (2019). 24-Gaze-Point Calibration Method for Improving the Precision of Ac-Eog Gaze Estimation. Sensors, 19.","DOI":"10.3390\/s19173650"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.jbiomech.2016.12.005","article-title":"Kinematic models of the upper limb joints for multibody kinematics optimisation: An overview","volume":"62","author":"Duprey","year":"2017","journal-title":"J. Biomech."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1109\/TNSRE.2014.2305111","article-title":"The extraction of neural information from the surface EMG for the control of upper-limb prostheses: Emerging avenues and challenges","volume":"22","author":"Farina","year":"2014","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."}],"container-title":["Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2218-6581\/9\/4\/83\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:17:59Z","timestamp":1760177879000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2218-6581\/9\/4\/83"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,9]]},"references-count":40,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["robotics9040083"],"URL":"https:\/\/doi.org\/10.3390\/robotics9040083","relation":{},"ISSN":["2218-6581"],"issn-type":[{"value":"2218-6581","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,9]]}}}