The Myokinetic Control Interface: How Many Magnets Can be Implanted in an Amputated Forearm? Evidence From a Simulated Environment

We recently introduced the concept of a new human-machine interface (the myokinetic control interface) to control hand prostheses. The interface tracks muscle contractions via permanent magnets implanted in the muscles and magnetic field sensors hosted in the prosthetic socket. Previously we showed the feasibility of localizing several magnets in non-realistic workspaces. Here, aided by a 3D CAD model of the forearm, we computed the localization accuracy simulated for three different below-elbow amputation levels, following general guidelines identified in early work. To this aim we first identified the number of magnets that could fit and be tracked in a proximal (T1), middle (T2) and distal (T3) representative amputation, starting from 18, 20 and 23 eligible muscles, respectively. Then we ran a localization algorithm to estimate the poses of the magnets based on the sensor readings. A sensor selection strategy (from an initial grid of 840 sensors) was also implemented to optimize the computational cost of the localization process. Results showed that the localizer was able to accurately track up to 11 (T1), 13 (T2) and 19 (T3) magnetic markers (MMs) with an array of 154, 205 and 260 sensors, respectively. Localization errors lower than 7% the trajectory travelled by the magnets during muscle contraction were always achieved. This work not only answers the question: “how many magnets could be implanted in a forearm and successfully tracked with a the myokinetic control approach?”, but also provides interesting insights for a wide range of bioengineering applications exploiting magnetic tracking.

[1]  Mao Li,et al.  A New Tracking System for Three Magnetic Objectives , 2010, IEEE Transactions on Magnetics.

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

[3]  J. J. Moré,et al.  Levenberg--Marquardt algorithm: implementation and theory , 1977 .

[4]  Carolyn M. Eng,et al.  Forearm Muscle Volumes Can Be Accurately Quantified From High Resolution Magnetic Resonance Imaging (MRI) , 2007 .

[5]  S. Gandevia,et al.  Limited independent flexion of the thumb and fingers in human subjects. , 1994, The Journal of physiology.

[6]  David Hankin,et al.  First-in-man demonstration of a fully implanted myoelectric sensors system to control an advanced electromechanical prosthetic hand , 2015, Journal of Neuroscience Methods.

[7]  G. Tomassini,et al.  Atlas of amputations and limb deficiencies: Surgical, prosthetic, and rehabilitation principles , 2005 .

[8]  Nadia Magnenat-Thalmann,et al.  Anatomical Modelling of the Musculoskeletal System from MRI , 2006, MICCAI.

[9]  Dario Farina,et al.  Extending mode switching to multiple degrees of freedom in hand prosthesis control is not efficient , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

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

[11]  Smith Dg,et al.  Atlas Of Amputations and Limb Deficiencies : Surgical, Prosthetic, and Rehabilitation Principles , 2004 .

[12]  Shriya S. Srinivasan,et al.  Towards functional restoration for persons with limb amputation: A dual-stage implementation of regenerative agonist-antagonist myoneural interfaces , 2019, Scientific Reports.

[13]  T. Fukunaga,et al.  In vivo Mechanical Properties of Proximal and Distal Aponeuroses in Human Tibialis Anterior Muscle , 2001, Cells Tissues Organs.

[14]  F. Clemente,et al.  The myokinetic control interface: tracking implanted magnets as a means for prosthetic control , 2017, Scientific Reports.

[15]  Christian Cipriani,et al.  Feasibility of Tracking Multiple Implanted Magnets With a Myokinetic Control Interface: Simulation and Experimental Evidence Based on the Point Dipole Model , 2020, IEEE Transactions on Biomedical Engineering.

[16]  Dario Farina,et al.  Simultaneous control of multiple functions of bionic hand prostheses: Performance and robustness in end users , 2018, Science Robotics.

[17]  M. Ortiz-Catalán,et al.  Restoring Natural Forearm Rotation in Transradial Osseointegrated Amputees , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[18]  Alessio Caciagli,et al.  Exact expression for the magnetic field of a finite cylinder with arbitrary uniform magnetization , 2018, Journal of Magnetism and Magnetic Materials.

[19]  R. Brent Gillespie,et al.  A regenerative peripheral nerve interface allows real-time control of an artificial hand in upper limb amputees , 2020, Science Translational Medicine.

[20]  Oskar Talcoth,et al.  Optimization of Sensor Positions in Magnetic Tracking , 2011 .

[21]  Christian Cipriani,et al.  Dexterous Control of a Prosthetic Hand Using Fine-Wire Intramuscular Electrodes in Targeted Extrinsic Muscles , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[22]  Brian A. Garner,et al.  Estimation of Musculotendon Properties in the Human Upper Limb , 2003, Annals of Biomedical Engineering.

[23]  Max Ortiz-Catalan,et al.  An osseointegrated human-machine gateway for long-term sensory feedback and motor control of artificial limbs , 2014, Science Translational Medicine.

[24]  Kristin L. Wood,et al.  Design Optimization of a Magnetic Field-Based Localization Device for Enhanced Ventriculostomy , 2016 .

[25]  N. Derby,et al.  Cylindrical magnets and ideal solenoids , 2009, 0909.3880.

[26]  Max Q.-H. Meng,et al.  Design and Optimization Strategy of Sensor Array Layout for Magnetic Localization System , 2017, IEEE Sensors Journal.

[27]  Wolfgang Birkfellner,et al.  Electromagnetic Tracking in Medicine—A Review of Technology, Validation, and Applications , 2014, IEEE Transactions on Medical Imaging.

[28]  Christian Cipriani,et al.  Development of an Embedded Myokinetic Prosthetic Hand Controller , 2019, Sensors.

[29]  Christopher M. Frost,et al.  Development of a Regenerative Peripheral Nerve Interface for Control of a Neuroprosthetic Limb , 2016, BioMed research international.

[30]  P. Pardalos,et al.  Handbook of global optimization , 1995 .

[31]  Zhi Li,et al.  Development of an architecturally comprehensive database of forearm flexors and extensors from a single cadaveric specimen , 2015, Comput. methods Biomech. Biomed. Eng. Imaging Vis..

[32]  Morad Askari,et al.  Upper Extremity Amputations and Prosthetics , 2015, Seminars in Plastic Surgery.

[33]  T. Fukunaga,et al.  Superficial aponeurosis of human gastrocnemius is elongated during contraction: implications for modeling muscle-tendon unit. , 2002, Journal of biomechanics.

[34]  Gary Tad Yamaguchi,et al.  Dynamic Modeling of Musculoskeletal Motion , 2001 .

[35]  Hugh M. Herr,et al.  Low-Latency Tracking of Multiple Permanent Magnets , 2019, IEEE Sensors Journal.

[36]  Martijn Froeling,et al.  Diffusion‐tensor MRI reveals the complex muscle architecture of the human forearm , 2012, Journal of magnetic resonance imaging : JMRI.