Share / Export Citation / Email / Print / Text size:

International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering , Engineering, Electrical & Electronic


eISSN: 1178-5608



VOLUME 9 , ISSUE 2 (June 2016) > List of articles


Ismail Ben Aabdallah * / Yassine Bouteraa * / Chokri Rekik *

Keywords : fuzzy controller, EMG signal, smart robot, HMI, features extraction, physical human-robot interaction.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 9, Issue 2, Pages 1,029-1,053, DOI:

License : (CC BY-NC-ND 4.0)

Received Date : 14-February-2016 / Accepted: 12-April-2016 / Published Online: 01-June-2016



Generally, the rehabilitation process needs a physical interactions between patients and therapists. Based on the principles governing such human-human interactions (HHI), the design of rehabilitation robots received several attempts in order to abstract the HHI in human-robot interaction (HRI). To achieve this goal, the rehabilitation robot should be smart and provides a useful and comprehensive platform to track the patient status. In this paper, a biofeedback-based high fidelity smart robot for wrist rehabilitation is designed. This robot is intended for repetitive exercises without therapist intervention. Hold the two sets of wrist movement: flexion/extension and radial/ulnar derivation. Distinguished by its compact mechanism design, the developed wrist rehabilitation robot (HRR) offers high stiffness with a total absence of any friction and backlash. Based on EMG signal, the smart robot can understand the patient pain degree. Two features extractions are used to estimate the pain level. A fuzzy logic controller is implemented in the LabVIEW-based human-machine interface (HMI) to determine the desired angle and velocity in real time. Parameters and results of each exercise can be stored and operated later in analysis and evolution of patient progress.

Content not available PDF Share



[1] 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.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] 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).
[8] 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.
[9] Christopher Scott, Liqiong Tang and Gourab Sen Gupta. “Bio-robotic system using bio-metric signals”. International Conference on Sensing Technology (ICST), Wellington, 3-5 Dec. 2013.
[10] 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.
[11] 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.
[12] Phinyomark, A., Phukpattaranont, P., & Limsakul, C. (2012c). Feature reduction and selection for EMG signal classification. Expert Systems with Applications, 39(8), 7420–7431.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] 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.
[18] 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.
[19] 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.
[20] 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.
[21] 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.
[22] 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.
[23] J. R. Cram, G. S. Kasman, and J. Holtz, “Introduction to Surface Electromyography”, 2nd ed. Jones and Bartlett Publishers, 2010.
[24] 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.
[25] 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.
[26] 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. 23–26, 2012, Satander, Spain.
[27] 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.
[28] 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.
[29] 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.
[30] 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.
[31] Englehart, K., & Hudgins, B. (2003). “A robust, real-time control scheme for multifunction myoelectric control”. IEEE Transactions on Biomedical Engineering, 50,7
[32] V.S. Huang, J.W. Krakauer. “Robotic neurorehabilitation: a computational motor learning perspective”. Journal of NeuroEngineering and Rehabilitation (2009), p. 6.
[33] 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.
[34] 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.
[35] 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.
[36] 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).
[37] 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.
[38] 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.
[39] 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.
[40] 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).
[41] 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).
[42] 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.
[43] 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.
[44] 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.
[45] Aschero, G., & Gizdulich, P. (2009). “Denoising of surface EMG with a modified Wiener filtering approach”. Journal of Electromyography and Kinesiology. 20 (2010) 366–373.
[46] 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.
[47] 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.
[48] 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.
[49] 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.