SoftHand Pro-D: Matching dynamic content of natural user commands with hand embodiment for enhanced prosthesis control

State of the art of hand prosthetics is divided between simple and reliable gripper-like systems and sophisticate hi-tech poly-articular hands which tend to be complex both in their design and for the patient to operate. In this paper, we introduce the idea of decoding different movement intentions of the patient using the dynamic frequency content of the control signals in a natural way. We move a step further showing how this idea can be embedded in the mechanics of an underactuated soft hand by using only passive damping components. In particular we devise a method to design the hand hardware to obtain a given desired motion. This method, that we call of the dynamic synergies, builds on the theory of linear descriptor systems, and is based on the division of the hand movement in a slow and a fast components. We use this method to evolve the design of the Pisa/IIT SoftHand in a prototype prosthesis which, while still having 19 degrees of freedom and just one motor, can move along two different synergistic directions of motion (and combinations of the two), to perform either a pinch or a power grasp. Preliminary experimental results are presented, demonstrating the effectiveness of the proposed design.

[1]  Robert D. Lipschutz,et al.  Targeted muscle reinnervation for real-time myoelectric control of multifunction artificial arms. , 2009, JAMA.

[2]  Ning Jiang,et al.  Extracting Simultaneous and Proportional Neural Control Information for Multiple-DOF Prostheses From the Surface Electromyographic Signal , 2009, IEEE Transactions on Biomedical Engineering.

[3]  Rob Ellis,et al.  Precision and power grip priming by observed grasping , 2007, Brain and Cognition.

[4]  Antonio Bicchi,et al.  On the role of hand synergies in the optimal choice of grasping forces , 2010, Auton. Robots.

[5]  Aaron M. Dollar,et al.  An investigation of grasp type and frequency in daily household and machine shop tasks , 2011, 2011 IEEE International Conference on Robotics and Automation.

[6]  J. F. Soechting,et al.  Postural Hand Synergies for Tool Use , 1998, The Journal of Neuroscience.

[7]  Manuel G. Catalano,et al.  Adaptive synergies: An approach to the design of under-actuated robotic hands , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Mark R. Cutkosky,et al.  On grasp choice, grasp models, and the design of hands for manufacturing tasks , 1989, IEEE Trans. Robotics Autom..

[9]  Toshio Tsuji,et al.  Bio-mimetic impedance control of an EMG-controlled prosthetic hand , 2000, Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113).

[10]  Etienne Burdet,et al.  Human Robotics: Neuromechanics and Motor Control , 2013 .

[11]  R. W. Young Evolution of the human hand: the role of throwing and clubbing , 2003, Journal of anatomy.

[12]  Antonio Bicchi,et al.  Modelling natural and artificial hands with synergies , 2011, Philosophical Transactions of the Royal Society B: Biological Sciences.

[13]  Manuel G. Catalano,et al.  Adaptive synergies for the design and control of the Pisa/IIT SoftHand , 2014, Int. J. Robotics Res..

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

[15]  L. Vainio,et al.  On the relations between action planning, object identification, and motor representations of observed actions and objects , 2008, Cognition.

[16]  Volkan Patoglu,et al.  Tele-impedance control of a variable stiffness prosthetic hand , 2012, 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[17]  Patrick van der Smagt,et al.  Surface EMG in advanced hand prosthetics , 2008, Biological Cybernetics.

[18]  G. Duan Analysis and Design of Descriptor Linear Systems , 2010 .

[19]  Marco P. Schoen,et al.  Control strategies for smart prosthetic hand technology: An overview , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[20]  Konrad Paul Kording,et al.  The statistics of natural hand movements , 2008, Experimental Brain Research.

[21]  Jacob L. Segil,et al.  Mechanical design and performance specifications of anthropomorphic prosthetic hands: a review. , 2013, Journal of rehabilitation research and development.

[22]  Philip R. Troyk,et al.  Implantable Myoelectric Sensors (IMESs) for Intramuscular Electromyogram Recording , 2009, IEEE Transactions on Biomedical Engineering.

[23]  David E. Meyer,et al.  Speed—Accuracy Tradeoffs in Aimed Movements: Toward a Theory of Rapid Voluntary Action , 2018, Attention and Performance XIII.