Virtual Training of the Myosignal

Objective To investigate which of three virtual training methods produces the largest learning effects on discrete and continuous myocontrol. The secondary objective was to examine the relation between myocontrol and manual motor control tests. Design A cohort analytic study. Setting University laboratory. Participants 3 groups of 12 able-bodied participants (N = 36). Interventions Participants trained the control over their myosignals on 3 consecutive days. Training was done with either myosignal feedback on a computer screen, a virtual myoelectric prosthetic hand or a computer game. Participants performed 2 myocontrol tests and 2 manual motor control tests before the first and after the last training session. They were asked to open and close a virtual prosthetic hand on 3 different velocities as a discrete myocontrol test and followed a line with their myosignals for 30 seconds as a continuous myocontrol test. The motor control tests were a pegboard and grip-force test. Main Outcome Measures Discrete myocontrol test: mean velocities. Continuous myocontrol test: error and error SD. Pegboard test: time to complete. Grip-force test: produced forces. Results No differences in learning effects on myocontrol were found for the different virtual training methods. Discrete myocontrol ability did not significantly improve as a result of training. Continuous myocontrol ability improved significantly as a result of training, both on average control and variability. All correlations between the motor control and myocontrol test outcome measures were below .50. Conclusions Three different virtual training methods showed comparable results when learning myocontrol. Continuous myocontrol was improved by training while discrete myocontrol was not. Myocontrol ability could not be predicted by the manual motor control tests.

[1]  C. K. van der Sluis,et al.  Learning to control opening and closing a myoelectric hand. , 2010, Archives of physical medicine and rehabilitation.

[2]  I. Dudkiewicz,et al.  Evaluation of prosthetic usage in upper limb amputees , 2004, Disability and rehabilitation.

[3]  Alcimar Soares,et al.  The Development of a Virtual Myoelectric Prosthesis Controlled by an EMG Pattern Recognition System Based on Neural Networks , 2004, Journal of Intelligent Information Systems.

[4]  C Marincek,et al.  Upper limb prosthetic use in Slovenia , 1994, Prosthetics and orthotics international.

[5]  David I. Anderson,et al.  Training with an upper-limb prosthetic simulator to enhance transfer of skill across limbs. , 2003, Archives of physical medicine and rehabilitation.

[6]  Jacob Cohen.  Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[7]  Linda Resnik,et al.  Using virtual reality environment to facilitate training with advanced upper-limb prosthesis. , 2011, Journal of rehabilitation research and development.

[8]  L. Fleming,et al.  Immediate, early, and late postsurgical management of upper-limb amputation. , 1984, Journal of rehabilitation research and development.

[9]  Kevin B. Englehart,et al.  A robust, real-time control scheme for multifunction myoelectric control , 2003, IEEE Transactions on Biomedical Engineering.

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

[11]  R. Weir,et al.  New Multifunctional Prosthetic Arm and hand Systems , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  Jamie I. D. Campbell,et al.  MorePower 6.0 for ANOVA with relational confidence intervals and Bayesian analysis , 2012, Behavior research methods.

[13]  M. Struwig,et al.  A gaming approach to learning medical microbiology: students’ experiences of flow , 2007, Medical teacher.

[14]  R. Jacob Vogelstein,et al.  WiiEMG: A real-time environment for control of the Wii with surface electromyography , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.

[15]  M. Arguin,et al.  Myocontrol in Aging , 2007, PloS one.

[16]  Andrew Jackson,et al.  Learning a Novel Myoelectric-Controlled Interface Task , 2008, Journal of neurophysiology.

[18]  Martin Arguin,et al.  Speed-accuracy trade-offs in myocontrol. , 2006, Human movement science.

[19]  Peter S. Lum,et al.  Trans-radial upper extremity amputees are capable of adapting to a novel dynamic environment , 2008, Experimental Brain Research.

[20]  A. Betker,et al.  Video game-based exercises for balance rehabilitation: a single-subject design. , 2006, Archives of physical medicine and rehabilitation.

[21]  Sanjay S Joshi,et al.  Two-Dimensional Cursor-to-Target Control From Single Muscle Site sEMG Signals , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[22]  Farbod Fahimi,et al.  Myoelectric training systems , 2011, Expert review of medical devices.

[23]  Caleb J. Behrend,et al.  Update on advances in upper extremity prosthetics. , 2011, The Journal of hand surgery.

[24]  T. Takeuchi,et al.  A Training System for Myoelectric Prosthetic Hand in Virtual Environment , 2007, 2007 IEEE/ICME International Conference on Complex Medical Engineering.

[25]  Christian Cipriani,et al.  Influence of the weight actions of the hand prosthesis on the performance of pattern recognition based myoelectric control: Preliminary study , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[26]  Michael Goldfarb,et al.  Design of a multifunctional anthropomorphic prosthetic hand with extrinsic actuation , 2009, 2009 IEEE International Conference on Rehabilitation Robotics.

[27]  R.J. Vogelstein,et al.  Air-Guitar Hero: A real-time video game interface for training and evaluation of dexterous upper-extremity neuroprosthetic control algorithms , 2008, 2008 IEEE Biomedical Circuits and Systems Conference.

[28]  Raoul M Bongers,et al.  Intermanual Transfer in Training With an Upper-Limb Myoelectric Prosthesis Simulator: A Mechanistic, Randomized, Pretest-Posttest Study , 2012, Physical Therapy.

[29]  R. Bakeman.  Recommended effect size statistics for repeated measures designs , 2005, Behavior research methods.

[30]  J. Algina,et al.  Generalized eta and omega squared statistics: measures of effect size for some common research designs. , 2003, Psychological methods.

[31]  Panagiotis Artemiadis,et al.  Embedded Human Control of Robots Using Myoelectric Interfaces , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[32]  A. Jackson,et al.  Flexible Cortical Control of Task-Specific Muscle Synergies , 2012, The Journal of Neuroscience.

[33]  Paolo Maria Rossini,et al.  Neuroplasticity in amputees: Main implications on bidirectional interfacing of cybernetic hand prostheses , 2009, Progress in Neurobiology.

[34]  Christian Cipriani,et al.  Abstract and Proportional Myoelectric Control for Multi-Fingered Hand Prostheses , 2013, Annals of Biomedical Engineering.