Cite

J.R. Potvin, L.R. Bent. “A validation of techniques using surface EMG signals from dynamic contractions to quantify muscle fatigue during repetitive tasks”. Journal of Electromyography and Kinesiology, 7 (2) (1997), pp. 131–139. Search in Google Scholar

Vukova, T., Vydevska-Chichova, M., & Radicheva, N. (2008). « Fatigue-induced changes in muscle fiber action potentials estimated by wavelet analysis”. Journal of Electromyography and Kinesiology, 18, 397–409.10.1016/j.jelekin.2006.09.01417287133 Search in Google Scholar

Marcello Mulas, Michele Folgheraiter and Giuseppina Gini. “An EMG-controlled Exoskeleton for Hand Rehabilitation”. Proceedings of the 9th International Conference on Rehabilitation Robotics June 28 - July 1, 2005, Chicago, IL, USA. Search in Google Scholar

Wonkeun Youn and Jung Kim. “Development of a Compact-size and Wireless Surface EMG Measurement System”. ICROS-SICE International Joint Conference 2009 August 18-21, 2009, Fukuoka International Congress Center, Japan. Search in Google Scholar

K.Y. Tong, S.K. Ho, P.M.K. Pang, X.L. Hu, W.K. Tam, K.L. Fung, X.J. Wei, P.N. Chen, M. Chen. “An Intention Driven Hand Functions Task Training Robotic System”. 32nd Annual International Conference of the IEEE EMBS Buenos Aires, Argentina, August 31 - September 4, 2010.10.1109/IEMBS.2010.562793021097247 Search in Google Scholar

Masahiro Kasuya, Masatoshi Seki, Kazuya Kawamura, Yo Kobayashi, Masakatsu G. Fujie, Fellow, Hiroshi Yokoi. “Robust grip force estimation under electric feedback using muscle stiffness and electromyography for powered prosthetic hand”. 2013 IEEE International Conference on Robotics and Automation (ICRA) Karlsruhe, Germany, May 6-10, 2013. Search in Google Scholar

Du, S., & Vuskovic, M. (2004). Temporal vs. spectral approach to feature extraction from prehensile EMG signals. In Proceedings of IEEE International Conference on Information Reuse and Integration (pp. 344–350). Search in Google Scholar

Matteo Rossi, Alessandro Altobelli, Sasha B Godfrey, Arash Ajoudani and Antonio Bicchi. “Electromyographic Mapping of Finger Stiffness in Tripod Grasp: a Proof of Concept”., 2015 IEEE International Conference on Rehabilitation Robotics (ICORR). Singapore, 11-14 Aug. 2015.10.1109/ICORR.2015.7281196 Search in Google Scholar

Christopher Scott, Liqiong Tang and Gourab Sen Gupta. “Bio-robotic system using biometric signals”. International Conference on Sensing Technology (ICST), Wellington, 3-5 Dec. 2013. Search in Google Scholar

Manoj Sivan, Justin Gallagher and Martin Levesley, Sophie Makower, David Keeling, Bipin Bhakta, Rory J O’Connor. Home-based Computer Assisted Arm Rehabilitation (hCAAR) robotic device for upper limb exercise after stroke: results of a feasibility study in home setting. Journal of NeuroEngineering and Rehabilitation 2014, 11:163.10.1186/1743-0003-11-163428004325495889 Search in Google Scholar

Jun-Uk Chu, Inhyuk Moon, and Mu-Seong Mun. “A Real-Time EMG Pattern Recognition based on Linear-Nonlinear Feature Projection for Multifunction Myoelectric Hand”. Proceedings of the 9th International Conference on Rehabilitation Robotics June 28 - July 1, 2005, Chicago, IL, USA. Search in Google Scholar

Phinyomark, A., Phukpattaranont, P., & Limsakul, C. (2012c). Feature reduction and selection for EMG signal classification. Expert Systems with Applications, 39(8), 7420–7431.10.1016/j.eswa.2012.01.102 Search in Google Scholar

Antonio Frisoli, Caterina Procopio, Carmelo Chisari, Ilaria Creatini, Luca Bonfiglio, Massimo Bergamasco, Bruno Rossi and Maria Chiara Carboncini. Positive effects of robotic exoskeleton training of upper limb reaching movements after stroke. Journal of NeuroEngineering and Rehabilitation 2012, 9:36.10.1186/1743-0003-9-36344343622681653 Search in Google Scholar

Christopher N Schabowsky, Sasha B Godfrey, Rahsaan J Holley, Peter S Lum. Development and pilot testing of HEXORR: Hand EXOskeleton Rehabilitation Robot. Journal of NeuroEngineering and Rehabilitation 2010, 7:36.10.1186/1743-0003-7-36292029020667083 Search in Google Scholar

Heather Daley, Kevin Englehart, Levi Hargrove, Usha Kuruganti. “High density electromyography data of normally limbed and transradial amputee subjects for multifunction prosthetic control”. Journal of Electromyography and Kinesiology, June 2012, Pages 478–484.10.1016/j.jelekin.2011.12.01222269773 Search in Google Scholar

Pei-Jarn Chen and Yi-Chun Du. Combining Independent Component and Grey Relational Analysis for the Real-Time System of Hand Motion Identification Using Bend Sensors and Multichannel Surface EMG. Mathematical Problems in Engineering. Volume 2015, Article ID 329783, 9 pages.10.1155/2015/329783 Search in Google Scholar

Rong Song, Kai-yu Tong, Xiaoling Hu and Wei Zhou. “Myoelectrically controlled wrist robot for stroke rehabilitation”. Journal of NeuroEngineering and Rehabilitation 2013, 10:52.10.1186/1743-0003-10-52368557023758925 Search in Google Scholar

Dario Farina, Ning Jiang, Hubertus Rehbaum, Aleš Holobar, Bernhard Graimann, Hans Dietl, and Oskar C. Aszmann. “The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges”. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 11 February 2014.10.1109/TNSRE.2014.230511124760934 Search in Google Scholar

Minas V. Liarokapis, Panagiotis K. Artemiadis and Kostas J. Kyriakopoulos. “Task Discrimination from Myoelectric Activity: A Learning Scheme for EMG-Based Interfaces”. International Conference on Rehabilitation Robotics (ICORR), Seattle, WA, 24-26 June 2013.10.1109/ICORR.2013.665036624187185 Search in Google Scholar

Tze-Yee Ho, Yuan-Joan Chen, Wei-Chang Hung, Kuan-Wei Ho and Mu-Song Chen. “The Design of EMG Measurement System for Arm Strength Training Machine”. Mathematical Problems in Engineering. Volume 2015, Article ID 356028, 10 pages.10.1155/2015/356028 Search in Google Scholar

J. Vogel, C. Castellini, and P. P. van der Smagt, “EMG-based teleoperation and manipulation with the DLR LWR-III.” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2011, pp. 672–678.10.1109/IROS.2011.6094739 Search in Google Scholar

Abhishek Gupta, Marcia K. O’Malley, Volkan Patoglu and Charles Burgar. Design, “Control and Performance of RiceWrist : A Force Feedback Wrist Exoskeleton for Rehabilitation and Training”. The International Journal of Robotics Research. 2008; 27; 233.10.1177/0278364907084261 Search in Google Scholar

J. R. Cram, G. S. Kasman, and J. Holtz, “Introduction to Surface Electromyography”, 2nd ed. Jones and Bartlett Publishers, 2010. Search in Google Scholar

Andrew Erwin, Marcia K. O’Malley, David Ress and Fabrizio Sergi. “Development, Control, and MRI-Compatibility of the MR-SoftWrist”. 2015 IEEE International Conference on Rehabilitation Robotics (ICORR). Singapore, 11-14 Aug. 2015. Search in Google Scholar

C. Pylatiuk, M. Müller-Riederer, A. Kargov, S. Schulz, O. Schill, M. Reischl and G. Bretthauer. “Comparison of Surface EMG Monitoring Electrodes for Long-term Use in Rehabilitation Device Control”. International Conference on Rehabilitation Robotics, Japan, June 23-26, 2009.10.1109/ICORR.2009.5209576 Search in Google Scholar

J. M. Hahne, H. Rehbaum, F. Biessmann, F. C. Meinecke, K.-R. Muller, N. Jiang, D. Farina, L. C. Parra. “Simultaneous and proportional control of 2D wrist movements with myoelectric signals”. 2012 IEEE international workshop on machine learning for signal processing, sept. 2326, 2012, Satander, Spain.10.1109/MLSP.2012.6349712 Search in Google Scholar

Angkoon Phinyomark, Pornchai Phukpattaranont, Chusak Limsakul. “Fractal analysis features for weak and single-channel upper-limb EMG signals”. Expert Systems with Applications 39 (2012) 11156-11163. Search in Google Scholar

Merletti, R., & Hermens, H. (2004).’’Detection and conditioning of the surface EMG signal”. In R. Merletti & P. Parker (Eds.), Electromyography: Physiology, engineering, and noninvasive applications (pp. 107-132). New Jersey: John Wiley & Sons. Search in Google Scholar

Yee Mon Aung and Adel Al-Jumaily. “Estimation of Upper Limb Joint Angle Using Surface EMG Signal”. Int. J. Adv. Robot. Syst., vol. 10, pp. 1-8.10.5772/56717 Search in Google Scholar

Babita Pandey, R.B. Mishra. “An integrated intelligent computing model for the interpretation of EMG based neuromuscular diseases”. Expert Systems with Applications 36 (2009)9201-9213. Search in Google Scholar

Englehart, K., & Hudgins, B. (2003). “A robust, real-time control scheme for multifunction myoelectric control”. IEEE Transactions on Biomedical Engineering, 50,710.1109/TBME.2003.81353912848352 Search in Google Scholar

V.S. Huang, J.W. Krakauer. “Robotic neurorehabilitation: a computational motor learning perspective”. Journal of NeuroEngineering and Rehabilitation (2009), p. 6.10.1186/1743-0003-6-5265349719243614 Search in Google Scholar

Jennifer L. Moore, Jason Raad, Linda Ehrlich-Jones, Allen W. Heinemann. “Development and Use of a Knowledge Translation Tool: The Rehabilitation Measures Databas”e. Archives of Physical Medicine and Rehabilitation. Volume 95, Issue 1, January 2014, Pages 197-202.10.1016/j.apmr.2013.09.010 Search in Google Scholar

Pei-Chi Hsiao, Shu-Yu Yang, Chung-Han Ho, Willy Chou, Shiang-Ru Lu. “The benefit of early rehabilitation following tendon repair of the hand: A population-based claims database analysis”. Journal of Hand Therapy. Volume 28, Issue 1, January-March 2015, Pages 20-26.10.1016/j.jht.2014.09.005 Search in Google Scholar

Ismail BENABDALLAH, Yassine BOUTERAA, Rahma BOUCETTA and Chokri REKIK. “Kinect-based Computed Torque Control for Lynxmotion robotic arm”. 2015 7th International Conference on Modelling, Identification and Control. Sousse, Tunisia, pp 1-6. Search in Google Scholar

Tkach, D., Huang, H., & Kuiken, T. A. (2010). “Study of stability of time-domain features for electromyographic pattern recognition”. Journal of NeuroEngineering and Rehabilitation, 7(21).10.1186/1743-0003-7-21 Search in Google Scholar

Beatriz Leon, Angelo Basteris, Gerdienke Prange, Francesco Infarinato, and Farshid Amirabdollahian, Patrizio Sale, Sharon Nijenhuis. “Grasps Recognition and Evaluation of Stroke Patients for Supporting Rehabilitation Therapy”. BioMed Research International Volume 2014, Article ID 318016, 14 page.10.1155/2014/318016 Search in Google Scholar

Zardoshti-Kermani, M., Wheeler, B. C., Badie, K., & Hashemi, R. M. (1995). “EMG feature evaluation for movement control of upper extremity prostheses”. IEEE Transactions on Rehabilitation Engineering, 3(4), 324–333.10.1109/86.481972 Search in Google Scholar

Haifa Mehdi, Olfa Boubaker. “Robot-assisted therapy: design, control and optimization”. International journal of smart sensing and intelligent systems, vol. 5, no. 4, december 2012.10.21307/ijssis-2017-522 Search in Google Scholar

Phinyomark, A., Hirunviriya, S., Limsakul, C., & Phukpattaranont, P. (2010). “Evaluation of EMG feature extraction for hand movement recognition based on Euclidean distance and standard deviation”. In Proceedings of 7th international conference on electrical engineering, electronics, computer, telecommunication, and information technology (pp. 856–860). Search in Google Scholar

Du, S., & Vuskovic, M. (2004). “Temporal vs. spectral approach to feature extraction from prehensile EMG signals”. In Proceedings of IEEE International Conference on Information Reuse and Integration (pp. 344–350). Search in Google Scholar

Rami N. Khushaba Sarath Kodagoda, Maen Takruri, Gamini Dissanayake. “Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals”. Expert Systems with Applications 39 (2012) 10731–10738. Search in Google Scholar

Angkoon Phinyomark, Franck Quaine, Sylvie Charbonnier, Christine Serviere, Franck Tarpin-Bernard, Yann Laurillau. “EMG feature evaluation for improving myoelectric pattern recognition robustness”. Expert Systems with Applications 40 (2013) 4832–4840. Search in Google Scholar

Boostani, R., & Moradi, M. H. (2003). “Evaluation of the forearm EMG signal features for the control of a prosthetic hand”. Physiological Measurement, 24(2), 309–319.10.1088/0967-3334/24/2/307 Search in Google Scholar

Aschero, G., & Gizdulich, P. (2009). “Denoising of surface EMG with a modified Wiener filtering approach”. Journal of Electromyography and Kinesiology. 20 (2010) 366–373. Search in Google Scholar

O J Lewis, R J Hamshere, and T M Bucknill. “The anatomy of the wrist joint”. Journal of Anatomy. 1970 May; 106(Pt 3): 539–552. Search in Google Scholar

M Avraam, M Horodinca, I Romanescu and A Preumont. “Computer Controlled Rotational MR-brake for Wrist Rehabilitation Device”. Journal of Intelligent Material Systems and structures, 2010.10.1177/1045389X10362274 Search in Google Scholar

Hu, X. L., Tong, K. Y., Song, R., Zheng, X. J., & Leung, W. W. (2009).” A comparison between electromyography-driven robot and passive motion device on wrist rehabilitation for chronic stroke”. Neurorehabilitation and Neural Repair, 23(8), 837-846.10.1177/1545968309338191 Search in Google Scholar

Silvestro Micera, S., Sabatini, A. M., Dario, P., & Rossi, B. (1999). “A hybrid approach to EMG pattern analysis for classification of arm movements using statistical and fuzzy techniques”. Medical Engineering and Physics, 21, 303–311.10.1016/S1350-4533(99)00055-7 Search in Google Scholar

eISSN:
1178-5608
Language:
English
Publication timeframe:
Volume Open
Journal Subjects:
Engineering, Introductions and Overviews, other