Controlling an upper-limb exoskeleton by EMG signal while carrying unknown load

Implementing an intuitive control law for an upper-limb exoskeleton dedicated to force augmentation is a challenging issue in the field of human-robot collaboration. The aim of this study is to design an innovative approach to assist carrying an unknown load without using force sensors or specific handle. The method is based on user's intentions estimated through a wireless EMG armband allowing movement direction and intensity estimation along 1 Degree of Freedom. This control law aimed to behave like a gravity compensation except that the mass of the load does not need to be known. The proposed approach was tested on 10 participants during a lifting task with a single Degree of Freedom upper-limb exoskeleton. Participants performed it in three different conditions : without assistance, with an exact gravity compensation and with the proposed method based on EMG armband. The evaluation of the efficiency of the assistance was based on EMG signals captured on seven muscles (objective indicator) and a questionnaire (subjective indicator). Results showed a statically significant reduction of mean activity of the biceps, erector spinae and deltoid by 20%±14%, 18%±12% and 25% ± 16% respectively while comparing the proposed method with no assistance. In addition, similar muscle activities were found both in the proposed method and the traditional gravity compensation. Subjective evaluation showed better precision, efficiency and responsiveness of the proposed method compared to the traditional one.

[1]  Oussama Khatib,et al.  Improved Force Control for Conventional Arms Using Wrist-Based Torque Feedback , 1995, ISER.

[2]  Panagiotis K. Artemiadis,et al.  An EMG-Based Robot Control Scheme Robust to Time-Varying EMG Signal Features , 2010, IEEE Transactions on Information Technology in Biomedicine.

[3]  Gennaro Raiola,et al.  Iterative virtual guides programming for human-robot comanipulation , 2017, 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM).

[4]  Joan M Stevenson,et al.  Mathematical and empirical proof of principle for an on-body personal lift augmentation device (PLAD). , 2007, Journal of biomechanics.

[5]  T. Ishida,et al.  A Robot Actuator Development With High Backdrivability , 2006, 2006 IEEE Conference on Robotics, Automation and Mechatronics.

[6]  Gabriele Bleser,et al.  Innovative system for real-time ergonomic feedback in industrial manufacturing. , 2013, Applied ergonomics.

[7]  Alison C McDonald,et al.  The effect of high pass filtering and non-linear normalization on the EMG-force relationship during sub-maximal finger exertions. , 2013, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[8]  F. Zajac.  Muscle and tendon: properties, models, scaling, and application to biomechanics and motor control. , 1989, Critical reviews in biomedical engineering.

[9]  Hiroshi Kobayashi,et al.  Muscle Suit Development and Factory Application , 2009, Int. J. Autom. Technol..

[10]  Goobong Chung,et al.  Development of a upper-limb exoskeleton robot for refractory construction , 2018 .

[11]  Kazuhiro Kosuge,et al.  Progress and prospects of the human–robot collaboration , 2017, Autonomous Robots.

[12]  Sofiane Boudaoud,et al.  Analysis of the sEMG/force relationship using HD-sEMG technique and data fusion: A simulation study , 2017, Comput. Biol. Medicine.

[13]  Stephen H. M. Brown,et al.  Less is more: high pass filtering, to remove up to 99% of the surface EMG signal power, improves EMG-based biceps brachii muscle force estimates. , 2004, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[14]  Khalil Ullah,et al.  A mathematical model for mapping EMG signal to joint torque for the human elbow joint using nonlinear regression , 2000, 2009 4th International Conference on Autonomous Robots and Agents.

[15]  Nicola Vitiello,et al.  Proportional EMG control for upper-limb powered exoskeletons , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[16]  Nicola Vitiello,et al.  Intention-Based EMG Control for Powered Exoskeletons , 2012, IEEE Transactions on Biomedical Engineering.

[17]  Nicolas Vignais,et al.  Controlling an Exoskeleton with EMG Signal to Assist Load Carrying: A Personalized Calibration , 2019, 2019 International Conference on Mechatronics, Robotics and Systems Engineering (MoRSE).

[18]  Giancarlo Ferrigno,et al.  Artificial neural network EMG classifier for functional hand grasp movements prediction , 2016, The Journal of international medical research.

[19]  C. DaSalla,et al.  Robot control using electromyography EMG signals of the wrist , 2005 .

[20]  Guillaume Morel,et al.  On the use of a base force/torque sensor in teleoperation , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[21]  Chang-Soo Han,et al.  The technical trend of the exoskeleton robot system for human power assistance , 2012 .

[22]  Hiroyuki Kobayashi,et al.  Quantitative Performance Analysis of Exoskeleton Augmenting Devices - Muscle Suit - for Manual Worker , 2011, Int. J. Autom. Technol..

[23]  Vincent Bonnet,et al.  Ergonomic contribution of ABLE exoskeleton in automotive industry , 2014 .

[24]  Pornchai Phukpattaranont,et al.  Evaluation of feature extraction techniques and classifiers for finger movement recognition using surface electromyography signal , 2018, Medical & Biological Engineering & Computing.

[25]  Bastien Berret,et al.  Interacting with a “Transparent” Upper-Limb Exoskeleton: A Human Motor Control Approach , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[26]  Xinjun Sheng,et al.  A prosthetic arm based on EMG pattern recognition , 2016, 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[27]  Guillaume Morel,et al.  Human force amplification with industrial robot : Study of dynamic limitations , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[28]  Michael J Agnew,et al.  An on-body personal lift augmentation device (PLAD) reduces EMG amplitude of erector spinae during lifting tasks. , 2006, Clinical biomechanics.

[29]  Philippe Garrec,et al.  DESIGN OF AN ANTHROPOMORPHIC UPPER LIMB EXOSKELETON ACTUATED BY BALL-SCREWS AND CABLES , 2010 .

[30]  Frank Krause,et al.  Exoskeletons for industrial application and their potential effects on physical work load , 2016, Ergonomics.

[31]  Goro Obinata,et al.  Development of a Wearable Robot for Assisting Carpentry Workers , 2006 .

[32]  David G Lloyd,et al.  Neuromusculoskeletal modeling: estimation of muscle forces and joint moments and movements from measurements of neural command. , 2004, Journal of applied biomechanics.

[33]  Daniela Rus,et al.  Sharing the Load: Human-Robot Team Lifting Using Muscle Activity , 2019, 2019 International Conference on Robotics and Automation (ICRA).

[34]  Joan M Stevenson,et al.  The effect of on-body lift assistive device on the lumbar 3D dynamic moments and EMG during asymmetric freestyle lifting. , 2008, Clinical biomechanics.