Design, control and validation of the variable stiffness exoskeleton FLExo

In this paper we present the design of a one degree of freedom assistive platform to augment the strength of upper limbs. The core element is a variable stiffness actuator, closely reproducing the behavior of a pair of antagonistic muscles. The novelty introduced by this device is the analogy of its control parameters with those of the human muscle system, the threshold lengths. The analogy can be obtained from a proper tuning of the mechanical system parameters. Based on this, the idea is to control inputs by directly mapping the estimation of the muscle activations, e.g. via ElectroMyoGraphic(EMG) sensors, on the exoskeleton. The control policy resulting from this mapping acts in feedforward in a way to exploit the muscle-like dynamics of the mechanical device. Thanks to the particular structure of the actuator, the exoskeleton joint stiffness naturally results from that mapping. The platform as well as the novel control idea have been experimentally validated and the results show a substantial reduction of the subject muscle effort.

[1]  M. Tomizuka,et al.  Control of Exoskeletons Inspired by Fictitious Gain in Human Model , 2009, IEEE/ASME Transactions on Mechatronics.

[2]  R. A. R. C. Gopura,et al.  Control methodologies for upper limb exoskeleton robots , 2012, 2012 IEEE/SICE International Symposium on System Integration (SII).

[3]  N. Özkaya,et al.  Testing apparatus and experimental procedure for position specific normalization of electromyographic measurements of distal upper extremity musculature. , 2001, Clinical biomechanics.

[4]  H. Devries.  MUSCLES ALIVE-THEIR FUNCTIONS REVEALED BY ELECTROMYOGRAPHY , 1976 .

[5]  Nicola Vitiello,et al.  NEUROExos: A variable impedance powered elbow exoskeleton , 2011, 2011 IEEE International Conference on Robotics and Automation.

[6]  G. Whitesides.  Soft Robotics. , 2018, Angewandte Chemie.

[7]  S. Micera,et al.  Evaluation of the effects of the Arm Light Exoskeleton on movement execution and muscle activities: a pilot study on healthy subjects , 2016, Journal of NeuroEngineering and Rehabilitation.

[8]  D. Rus,et al.  Design, fabrication and control of soft robots , 2015, Nature.

[9]  Matteo Bianchi,et al.  Controlling Soft Robots: Balancing Feedback and Feedforward Elements , 2017, IEEE Robotics & Automation Magazine.

[10]  R. A. R. C. Gopura,et al.  Development and Control of Upper-Limb Exoskeleton Robots , 2009 .

[11]  Lihua Huang,et al.  On the Control of the Berkeley Lower Extremity Exoskeleton (BLEEX) , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[12]  F. Mohd-Yasin,et al.  Techniques of EMG signal analysis: detection, processing, classification and applications , 2006, Biological Procedures Online.

[13]  Carlo J. De Luca,et al.  The Use of Surface Electromyography in Biomechanics , 1997 .

[14]  Roberto Merletti,et al.  The extraction of neural strategies from the surface EMG. , 2004, Journal of applied physiology.

[15]  Giorgio Grioli,et al.  The Quest for Natural Machine Motion: An Open Platform to Fast-Prototyping Articulated Soft Robots , 2017, IEEE Robotics & Automation Magazine.

[16]  P. Zipp,et al.  Recommendations for the standardization of lead positions in surface electromyography , 1982, European Journal of Applied Physiology and Occupational Physiology.

[17]  C. Majidi.  Soft Robotics: A Perspective—Current Trends and Prospects for the Future , 2014 .

[18]  Nikolaos G. Tsagarakis,et al.  Tele-Impedance based stiffness and motion augmentation for a knee exoskeleton device , 2013, 2013 IEEE International Conference on Robotics and Automation.

[19]  Günter Hommel,et al.  TORQUE CONTROL OF AN EXOSKELETAL KNEE WITH EMG SIGNALS , .

[20]  Cosimo Della Santina,et al.  Soft Robots that Mimic the Neuromusculoskeletal System , 2017 .

[21]  Agnes Roby-Brami,et al.  Upper-Limb Robotic Exoskeletons for Neurorehabilitation: A Review on Control Strategies , 2016, IEEE Reviews in Biomedical Engineering.

[22]  F. G. Evans,et al.  Anatomical Data for Analyzing Human Motion , 1983 .

[23]  Nikolaos G. Tsagarakis,et al.  VSA-CubeBot: A modular variable stiffness platform for multiple degrees of freedom robots , 2011, 2011 IEEE International Conference on Robotics and Automation.

[24]  J. F. Yang,et al.  Electromyographic amplitude normalization methods: improving their sensitivity as diagnostic tools in gait analysis. , 1984, Archives of physical medicine and rehabilitation.

[25]  Nicola Vitiello,et al.  NEUROExos: A Powered Elbow Exoskeleton for Physical Rehabilitation , 2013, IEEE Transactions on Robotics.

[26]  Dario Farina,et al.  EMG-Driven Forward-Dynamic Estimation of Muscle Force and Joint Moment about Multiple Degrees of Freedom in the Human Lower Extremity , 2012, PloS one.

[27]  P. Beyl,et al.  The MACCEPA actuation system as torque actuator in the gait rehabilitation robot ALTACRO , 2010, 2010 3rd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics.

[28]  Manuel G. Catalano,et al.  Variable impedance actuators: A review , 2013, Robotics Auton. Syst..

[29]  Heike Vallery,et al.  Stable and User-Controlled Assistance of Human Motor Function , 2009 .

[30]  D J Ostry,et al.  Are complex control signals required for human arm movement? , 1998, Journal of neurophysiology.